Final-Project-Phase5.ipynb

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    "# Final Project \n",
    "\n",
    "**Name/NetIDs:** Maywa Padungrat (tp267), Brendan Lo (bl536), Shirley Lee (yl3322), Tayapa Ungsrisawasdi (tu52) \n",
    "\n",
    "### Introduction \n",
    "\n",
    "For the purposes of this study, we are going to explore how age and wealth affect the marriage rates in the United Kingdom, Italy, Germany, Austria, Switzerland, Japan, Mexico, Canada, United States, and Australia. We will also explore how cohabitation and birth outside of marriage affect the marriage rates in the United States.Then, we will compare the results within and between each country. To further enhance the effect of age and wealth on marriage rates, we will be comparing the marriage rates between countries with similar population sizes and between countries within different continents to limit the effects of confounding variables like population size and possibly cultural differences.\n",
    "\n",
    "\n",
    "### Research Questions\n",
    "- What factors (age and wealth) show the strongest correlation with marriage rates for European countries?\n",
    "- What factors (age, wealth, cohabitation, or birth outside of marriage) show the strongest correlation with marriage rates in the U.S.?\n",
    "- What factors (age and wealth) show the strongest correlation with marriage rates in relation to population size?\n",
    "- Does each continent display the same relationship (positive/negative/neutral) between these factors and marriage rates?\n",
    "- Does each country display the same relationship (positive/negative/neutral) between these factors and marriage rates?\n",
    "- Do countries with similar population sizes display the same relationship (positive/negative/neutral) between these factors and marriage rates?\n",
    "- How does each factor, individually, affect marriage rates in each continent? \n",
    "- How does each factor, individually, affect marriage rates in each country? \n",
    "- How does each factor, individually, affect marriage rates in each population size group?\n",
    "- Is there a correlation between age and wealth?\n",
    "- How do historical marriage rates (in the last 30 years) compare to recent marriage rates in those countries?\n",
    "- How do these changes compare between continents? Which continent is experiencing the biggest change in marriage rates within the last 30 years?\n",
    "- How do these changes compare between countries? Which country is experiencing the biggest change in marriage rates within the last 30 years?\n",
    "- Is there a correlation between population size and marriage? \n",
    "- Is there a correlation between cohabitation and birth outside of marriage?\n",
    "\n",
    "As a follow-up to this study, one may possibly look into the different “cultural factors” or factors associated with population sizes to further understand the effects of it on marriage rates in a more in-depth analysis. \n",
    "\n",
    "\n",
    "### Preregistration Statement \n",
    "\n",
    "**Hypothesis 1**: Countries within greater proximities will exhibit higher correlations between average marriage age and marriage rates. \n",
    "\n",
    "**Hypothesis 2**: Countries with similar population sizes will exhibit higher correlations between average marriage and marriage rates, although not as high compared to countries within greater proximities. \n",
    "\n",
    "\n",
    "### Other Hypotheses \n",
    "- **Hypothesis 3**: We predict that wealth would be the factor that shows the strongest correlation with marriage rates in the United States. \n",
    "\n",
    "- **Hypothesis 4**: We predict that each country would display the same/similar relationship between average marriage age and marriage rates. \n",
    "\n",
    "- **Hypothesis 5**:However, we predict that there won't be a significant correlation or similarity based on population size. \n",
    "\n",
    "- **Hypothesis 6**: We believe that there wouldn't be a correlation between population size and marriage. \n",
    "\n",
    "- **Hypothesis 7**: We also believe that there could be a correlation between the factors. We believe that there could be a strong correlation between cohabitation and birth outside of marriage and a strong correlation betwen age and wealth.\n",
    "\n",
    "\n",
    "### Final Data\n",
    "\n",
    "**Raw Data:**\n",
    "\n",
    "You can find the raw source data from https://ourworldindata.org/marriages-and-divorces.\n",
    "\n",
    "***What are the observations (rows) and the attributes (columns)?***\n",
    "\n",
    "For the cleaned marriage_df dataset, we have columns of Entity (Country) and Year which yields the observation of Crude Marriage Rate. \n",
    "Similarly, for cleaned age_df, we have the same columns but with observation of Average Marriage Age. \n",
    "In our cleaned networth_df, we get the observation of Total Net Worth to the according attribute of dates (year:quarter).\n",
    "Furthermore, our outside_birth_df has different years as rows and birth outside of marriage in percentage (% of all births) as our columns. \n",
    "In our Cohabitation_df, the columns are years (named as ‘Years’) and cohabitation rates (named as ‘Percent_num’), where the rows display cohabitation rates observations for each year from 1990-2021.\n",
    "\n",
    "\n",
    "***Why was this dataset created?***\n",
    "\n",
    "Our World in Data, a scientific online publication presented the data behind fast changes in marriages where they are becoming less common, people marrying later, unmarried couples increasingly choosing to live together, and increasing rate of divorce. It elaborates on some main drivers to these widespread changes.\n",
    "\n",
    "\n",
    "***Who funded the creation of the dataset?***\n",
    "\n",
    "Research by Our World in Data is supported by grants from the Quadrature Climate Foundation, the Bill and Melinda Gates Foundation, and a grant from the German entrepreneur, businesswoman and philanthropist Susanne Klatten.\n",
    "\n",
    "\n",
    "***What processes might have influenced what data was observed and recorded and what was not?***\n",
    "\n",
    "There are several factors that might have influenced what data was observed and recorded. First, there might have been a limitation in data collection due to accessibility. The national-wide data collection would have been unable to collect all the data from rural areas where there is a lack of labor and slow tech development. \n",
    "Furthermore, the rates that we have observed could have been influenced by the pandemic throughout 2019-2022 which is not taken into consideration in the raw dataset. \n",
    "Lastly, there isn't every data from each country for each year in our raw data which estimates the trend of each observation. \n",
    "\n",
    "\n",
    "***What preprocessing was done, and how did the data come to be in the form that you are using?***\n",
    "\n",
    "The dataset that we get from the source relies on multiple sources from different countries such as by continent. After preprocessing the data, we could access the combined / organized dataset for the entire global population.\n",
    "\n",
    "***If people are involved, were they aware of the data collection and if so, what purpose did they expect the data to be used for?***\n",
    "\n",
    "Our data collection differs by continent or country coming from census data, survey data, historical records, and national statistical agencies. For instance, European data comes from the Eurostat dataset where utilizes data from statistical authorities in EU countries while the US dataset comes from Carter et al. (2006) for the period of 1920-1995, the US Census Bureau (2007) for the period 1996-2004 and CDC for the period 2005 to present. For other non-European countries that are OECD members, the data is collected from the OECD Family Database. Lastly, the UN World Marriage Database is used for all other countries. \n",
    "These datasets are collected directly through censuses, surveys, a set of questionnaires or from local jurisdictions and territories. Thus, subjects were likely to have been aware of the data collection expecting their responses being used for further research or national data usage.\n",
    "\n",
    "\n",
    "### Data Uses\n",
    "\n",
    "The dataset has been used for several purposes. For instance, it had been referenced for a book titled Rise and Fall: A Discourse Upon the Phenomena of Civilisation and Decline, that dive into the “cyclical patterns of civilization.” It has also been cited in publications that focus on “divorce and separation filings” in certain countries such as the Decline in Rate of Divorce and Separation Filings in Denmark in 2020 Compared with Previous Years. \n",
    "\n",
    "The data should not be used for any experimental designs because there are no controlled variables and none of the marriage rates or factors in the raw data are measured in a controlled environment. Therefore, a lot of confounding variables may be present so one can not use the data to conclude on a causal relationship.\n",
    "\n",
    "\n",
    "### Data Limitations\n",
    "\n",
    "The dataset on population, age and marriage filings is collected from reputable sources such as the United Nations. The United Nations obtains its information from national statistical offices, which collects census data from administrative recording systems. Therefore, the dataset theoretically contains all possible instances for age and marital status. \n",
    "\n",
    "The dataset on U.S. cohabitation rates for partners, however, does not contain all possible instances as it is based on a sample. The data is obtained from the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS). The notice is mailed and telephone/in-person interviews are conducted for the sample. Because of this, there may be a huge sampling error that could arise from nonresponse and other obstacles such as language barriers and for those who are difficult to locate. In addition, the data is limited to those under 18 years of age and cohabitation is defined as POSSLQ (persons of opposite sex sharing living quarters). Therefore, this excludes relationships where individuals are under 18 and partners are of the same sex, which could heavily underestimate the cohabitation rate. This could blur the relationship between cohabitation rates and marriage rates in the U.S., especially when same-sex marriage is legalized. \n",
    "\n",
    "For U.S. births outside of marriage, the data were collected from Centres for Disease Control and Prevention where the dataset should contain all possible instances for births that are recorded, especially at hospitals. However, not all births would have been recorded in areas with lack of accessibility such as technology where the center cannot obtain all information. Furthermore, CDC receives data from 50 states and 3000+ local jurisdictions and territories under agreements. Thus, it is up to each city, county, and state to decide which information to share where we see nonresponse challenges throughout the data collection. \n",
    "\n",
    "Nevertheless, there may be some marriage filings that are not officially recorded nor individuals living in the country without records in the government system. The census in each country, for instance, can also undercount or overcount the population. Those who are undercounted are more likely to be in poverty, not have access to the internet, or move around a lot. Those who are overcounted, on the other hand, are more likely to come from white and richer backgrounds as it often happens when one is counted both at home and at school. \n",
    "\n",
    "Therefore, this could cause a certain skew in the data where wealthier people comprise a larger percentage of the population than the actual value. Since a higher number of relatively affluent people are recorded in the data, the graphs could demonstrate a stronger correlation between wealth and marriage rates than the true nature. The same could apply to marriage filings, where the poor officially report them less compared to the wealthy. Thus, there could be an underestimation for the relatively poor population in terms of the number of marriage filings. \n",
    "\n",
    "The United Nations World Marriage Data could have collected users’ names and addresses as well but it stated that “such information will be used only for statistical purposes and will not be published for general access.” Also, there is confidential partnership status data that they exclude from the dataset, causing some needs for rounding (to add up to 100%). This could further create some bias in the data as it is not the true representation of the entire population of a country.\n"
   ]
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  {
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   "id": "ae2b228e",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import sklearn \n",
    "\n",
    "import seaborn\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "\n",
    "%load_ext sql\n",
    "\n",
    "%config SqlMagic.autopandas = True\n",
    "%config SqlMagic.feedback = False\n",
    "%config SqlMagic.displaycon = False\n",
    "\n",
    "%sql duckdb:///:memory:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fefee7ce",
   "metadata": {},
   "source": [
    "Here, we created a general function calc_summary_stats to calculate and return the mean, standard deviation, and median of a given input data. Throughout our project, we had many different factors and datasets we worked with, so this function helps reduce redundancy when calculating summary statistics for different factors or datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cfd311a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_summary_stats(df):\n",
    "    print (f\"Summary Stats for {df.name}:\\n\", f\"Mean: {np.mean(df)}\\n\", f\"Standard Deviation: {np.std(df)}\\n\", f\"Median: {np.median(df)}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "f0bc0b6e",
   "metadata": {},
   "source": [
    "## Data Cleaning: marriage_df (Crude Marriage Rates)\n",
    "\n",
    "In order to clean the data, we changed the name of Crude Marriage Rate column from \"Crude marriage rate (per 1,000 inhabitants)\" to \"CrudeMarriageRate\" to avoid any SQL errors.  \n",
    "\n",
    "In terms of data cleaning, the \"code\" column was removed because it wasn't relevant for data analysis/exploration as it was redundant. ALB = Albania or Vietnam = VNM and we're planning to analyze the geography so the code wasn't necessary. \n",
    "\n",
    "The dataset had 183 different countries and plotting all the data in one graph would be difficult to analyze. Therefore, at first, we wanted to limit the data to only the United States, United Kingdom and Hong Kong (country in Asia) to get a diverse range of data/countries. However, we realized that Hong Kong is a smaller country which could be an outlier. \n",
    "\n",
    "As a result, we decided to manually list out all the countries in the dataset to see how large our dataset is for marriage_df and age_df, the population, and the continent. We wanted to compare in a broad manner so we decided to plot all of them together and later analyze specific countries that may have a similar trend based on population and geography (could be influenced by culture). The countries includes UK, Italy, Germany, Austria, Switzerland, Japan, Mexico, Canada, and Australia. \n",
    "\n",
    "## Data Description: marriage_df (Crude Marriage Rates)\n",
    "For the cleaned marriage_raw_df dataset, we have columns of Entity (Country) and Year which yields the observation of Crude Marriage Rate. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c1d93c4",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>CrudeMarriageRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1960</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1961</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1962</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1963</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1964</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3246</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>VNM</td>\n",
       "      <td>2002</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3247</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>WLF</td>\n",
       "      <td>1970</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3248</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>1970</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YEM</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>YEM</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3251 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Entity Code  Year  CrudeMarriageRate\n",
       "0               Albania  ALB  1960                7.8\n",
       "1               Albania  ALB  1961               11.3\n",
       "2               Albania  ALB  1962                7.5\n",
       "3               Albania  ALB  1963                7.5\n",
       "4               Albania  ALB  1964                7.2\n",
       "...                 ...  ...   ...                ...\n",
       "3246            Vietnam  VNM  2002               11.8\n",
       "3247  Wallis and Futuna  WLF  1970                6.3\n",
       "3248     Western Sahara  ESH  1970                6.6\n",
       "3249              Yemen  YEM  2001                0.5\n",
       "3250              Yemen  YEM  2002                0.6\n",
       "\n",
       "[3251 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marriage_raw_df = pd.read_csv('marriage-rate-per-1000-inhabitants.csv')\n",
    "marriage_raw_df.columns\n",
    "\n",
    "marriage_raw_df[\"Year\"] = marriage_raw_df[\"Year\"].astype(int)\n",
    "\n",
    "marriage_raw_df = marriage_raw_df.rename(columns={\"Crude marriage rate (per 1,000 inhabitants)\" : \"CrudeMarriageRate\"})\n",
    "marriage_raw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8dc48b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique values 183\n"
     ]
    }
   ],
   "source": [
    "unique_vals = len(pd.unique(marriage_raw_df['Entity']))\n",
    "print(\"No. of unique values\", unique_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9c14077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable marriage_df\n",
      "Returning data to local variable marriage_df\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Year</th>\n",
       "      <th>CrudeMarriageRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1981</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1982</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1983</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1984</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>United States</td>\n",
       "      <td>1949</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>United States</td>\n",
       "      <td>1950</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>United States</td>\n",
       "      <td>1938</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>United States</td>\n",
       "      <td>1963</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>United States</td>\n",
       "      <td>1943</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Entity  Year  CrudeMarriageRate\n",
       "0        Australia  1980                7.4\n",
       "1        Australia  1981                7.6\n",
       "2        Australia  1982                7.7\n",
       "3        Australia  1983                7.5\n",
       "4        Australia  1984                7.0\n",
       "..             ...   ...                ...\n",
       "556  United States  1949               10.6\n",
       "557  United States  1950               11.1\n",
       "558  United States  1938               10.3\n",
       "559  United States  1963                8.8\n",
       "560  United States  1943               11.7\n",
       "\n",
       "[561 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql marriage_df << SELECT Entity, Year, CrudeMarriageRate FROM marriage_raw_df GROUP BY entity, year, CrudeMarriageRate\n",
    "%sql marriage_df << SELECT * FROM marriage_df WHERE Entity IN ('United States', 'United Kingdom', 'Italy', 'Germany', 'Austria', 'Switzerland', 'Japan', 'Mexico', 'Canada', 'Australia') \n",
    "marriage_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba12fc5",
   "metadata": {},
   "source": [
    "### Summary Statistics\n",
    "\n",
    "We used calc_summary_stats to find the mean, median, and standard deviation of our raw marriage rate data (which included marriage rates from all 183 countries) as well as our cleaned marriage rate data (which included the 9 countries we are interested in comparing). From our summary statistics, we can see that by focusing on the crude marriage rate for just the 9 countries, the median stays the same, the average increases slightly by 0.1, and the standard deviation decreases significantly by 0.7. The most significant change in statistic is the decrease in standard deviation– it indicates that after narrowing the amount of countries we're looking at, our data is less spread out and more clustered around the mean, resulting in more accurate estimates if we were to estimate a parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2afc5a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Marriage Rate Data (All 181 Countries)\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 6.694801600780998\n",
      " Standard Deviation: 2.6369614645313004\n",
      " Median: 6.5\n",
      "\n",
      "Cleaned Marriage Rate Data (9 Specified Countries)\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 6.824598932354727\n",
      " Standard Deviation: 1.9397798978930487\n",
      " Median: 6.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Raw Marriage Rate Data (All 181 Countries)\")\n",
    "calc_summary_stats(marriage_raw_df['CrudeMarriageRate'])\n",
    "print(\"\\nCleaned Marriage Rate Data (9 Specified Countries)\")\n",
    "calc_summary_stats(marriage_df['CrudeMarriageRate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044c746c",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Crude Marriage Rate for the 9 countries against the year.\n",
    "<br> <br>\n",
    "Here, we could see a decreasing trend in crude marriage rate globally throughout the years. Looking at the graph, even though there is a holistic trend of decreasing crude marriage rate, there are three periods where most of the countries on our list of countries saw abrupt increases in the rate: around 1970, 2000, and 2015. In addition, there are some countries where they saw exponential increase in the rate going against the global trend such as Austria around 1985. In overall, we could see a negative trend in crude marriage rate from 1960 to 2020 from the majority of countries on our list with noticeable increase in three periods of time and one outlier with increased crude marriage rate over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c593cb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EdeVqGlk8TAhxDDgFbATisc0ULomUchOQdcGxtVLKMuf4HUBgTdq/UqxS/ieDngrNFoQAZ03lCQPUwiYfUZn7Z9n9sqsiXwGcmxGYlKUhBYWbnppuFr8NdAWOSilDgVuBrVfZ9v1UY0yulqQSI8eLLt4sre8YpEQnxCWDwoQQ6C/h/llSekx/KaGhUsqMiSIzoaBw81NTQ2CSUmYCKiGESkq5AWh3pY0KIV4BzMC8Kso8LISIFkJEp6enX1E7dqUCa+b/2MOqxFp1BjIolZKudEYgUYlzI/5LIYQo9xxSUFC4uampIcgRQjgBm4B5QojPsT3ILxshxL3YNpEnyCo2KKSUM6WUEVLKCG9v7ytpqnxU+19aHpJSYrTKakf0epVNjvrCr6DMiIgaJKbXKnLUCgr1gpoaghFAMfA0sAabx8+lI6IugRBiEDYV0+FSyovdWWqZMo+Ykv/Qw8r2cKf6GYFQgbxYL8hgldhVY0TO1aHMCBQU6gM1MgRSykIppUVKaZZSzgXWAu9XdY0QYj6wHVsMQpIQ4gHgK2yqpeuEEPuEEN9dZf+rRCcEQvy3ZgRlI/SazAjOLw82t1GTVVZrRMrQli69KXkJFBRubqoLKGsjhFgrhIgRQrwthPAVQiwG/gGq1E6WUt4lpfSXUmqllIFSyh+llE2klEFSynalr6m1+WEq6T96lajTGUFdy1BXJTFdGTXd7C3zCjpfJqKk3GOo5jMCUDyHFBRudqob+v2ALahsDJAO7AFOAk2klJ/Wcd9qBTuVqvwBVxfUtQz1pSSmL0XZZq+mmjV+jUpgpxbkWyznrrWUzSZqOiNQEtQoKNQHqosE0ksp55T+fkQI8RwwXUppqeKaa8/q6ZB6sNJT/tKWr1eqVYgqFDUvwi8cBr9XZZFrIUOdn59fqcR0kyZN+OWXX3BwcKjQJ8NlbPY6qdVkmszl+QdKrFaEqH42UUb5jEAxBAoKNzXVGQI7IUR7KH+CFgBtROlTRkp5ad3lGwSbnLLEClQeXnXlXAsZ6hkzZpQfP19i+tVXX+XHH3/kiSeeqNCOwSpxrEQ2ojKcNWoyjGYKLVacNWoMVolOJWpkRODcjEBZGlJQuLmpzhCkAJ+c9z71vPcS6FcXnbpsqhi5myxWThSW0NBeh3stSCGcz7WQoT6f8yWmCwoKGDhwYIXz1rLNXm3NDIGjWgUCCswWnDVqSqxW7GtoRMCW01gjFBdSBYWbnSqfjFLKvteqI3WFXiVAUOv7BGUy1DExMQghsFgsCCEYPnx4lTLU//77L7///jtfffUV69evr7Tu82Woz2fy5MnMWbiINm3bsnzer0RGRlY4b6ihx1AZaiFwUKnIt1jxLY0/cNNexvIZtlmBqYokNwoKCjc+NdUachBCvCqEmFn6vqkQ4rLjCK4HKiHQV5Oa8Uq4VjLU55Ofn4/08ORMYRHz5l0clG2oocfQ+ThpVBRbrOXeQ9VpDF2ITpkRKCjc9NR0reQnYDfQrfR9ErAQWFkXnapt9CoVJbWcTetayVCfz0szZjChbx/8Gzaka9s2FBQUVDh/bkZQ84e5s1rNWcxkmmyB4pdrCLQqQb7FFqFc070FBQWFG4sayVALIaKllBFCiL1Syvalx/ZLKdvWeQ+5ehnqlBIjZ01mwp3sLynEdjNwqshAntnmsNXYQY/TBeqiicUG8i1WWjnZ17hOq5TEFBQDICWEO1/ePTprMJFiMNHKyR6NSlBssZJhNBFgp7usehQZagWF2qdWZagBoxDCHtsGMUKIxsBNI+upV5fKKVxiCcNwE+TeNVsl+RYL7lrbw7+yfMGGGmgMXYhKCBzVKqS0CdFdrqHUXeA5lGYwkWWyUHgT3FMFBQUbNTUEb2DTGAoSQswD/gVeqLNe1TJ2VYjP5ZktxBWWkG++sUIjLiTXbEFK8NJp0KoERZV8FkMNVEcrw1ltMy5Xcm15pjKrFaPVSm7pfcy7we+ngoLCOWq0RyClXCeE2IMtJ4EAnpRSZtRpz2qRsgdciVXiesG5sgdXtsl8yUQuNwI5ZjM6lcBepcJBrbpoRmC2SiwS9Few9OWkUYGh5tIS53P+jKCobJ9BrSLPbKGBsm9AgdnC80dO08bZgUkNPHG8gf/GFP671NRrqAMQjC2uIBloKIRoLISoXcf8OkItBFqVuGhGIKUsH7nmmi03rHiayWqlwGzFTatGlLp8mqyyQkTvlXgMlWGvUuGl05QvO10OmnJhP0mW0YKLRo2nVo3RKmvdU+tm5PXjZ1h6Noc3TyQTsf0wH59KpVCZLSncYNR0LeAbbKklZ2LTH9oO/A4cLc1DfMNTmfhcsdWK2Spx06qxyht3OSOntF/uGpvddSgN+io+z7AZ5OV7DJUhhCDAToe9+vINQVmCmmyTGbOUeOo0uJSOem/U+3mtWJOey28pWTzR0IeVHZoS4erIh/GpTDmccFEeCAWF60lNnxrxQPvSRDEdgfZADHAb8EEd9a1WKROfO3/Un2e2PUgb6LVohCh/4N5ISCnJNlmwU4vy/Apl0b9F5wnGlVisIM4t1VxLtCqBpXSz2VmtQqdSlS8P/VdJN5p49shpWjvZ83yoHxGujvzSphFvNQngn8w8fk3JvN5dVFAop6ZLO82llIfK3kgpDwsh2kspT94sa8BlujpZJjNeOi1gG7E6qFVoVSpctWqyTGYsUqKu4WdKTU3lqaeeYteuXej1ekJCQvjss88ICwurtX5nmMwUW6wE2ulwcnKioKAAtRDo1YKiUrVQS6mxcFKrrot7rE4ICrFtZJf9PbhoVJw1mDFbJZrrYJyuBQarla3ZBZVqLc05k0GBxcJXLRujO2+W9kCgF+syc3n9WDLd3Zxp5KC/6n5YpWRbTkGlnloC6OTqWOvyKgr1i5r+dRwRQnyLbTkIYDy2ZSE9YKqTntUyzmrbJutZoxkPrQaLlBRbrPjpbUbBXaMm02gm12zBowb/NFJKRo0axb333lsuQb1v3z7S0tJqzRCUWKykGEw4a9R4XLB+76CyjbillGQYbcsyfjpdrbR7uehVKlTCUr50BeCiUXPWYLa5vKrq30OowGxh0sFTbMspuGSZt5sG0NyxYkyHSgg+b9GQvlFHeDw2gT/bN70qQ2mRkmfiTrMgNeuSZRzVKiYHeDEl0Buf0r93BYXzqel/6GTgUeApbIOMLcBz2IzAddcjej/qfeKyqk8MY5GSYqu0edaUbnA6qFSU/R8WWayohG0ZqblHc17s/OIl69qwYQNarZapU8/l1mnXrh0FBQXceuutZGdnYzKZePvttxkxYgTx8fEMHjyYHj16sG3bNgICAli+fDn29vaVykvb2duzNe4oz9x3L1ppZfCgQeXtFBQUcM+w4aRnZaGyWpjyymsMHT7iunmkeOs0eGo1FR5oDioVGpUgz2ypd6PRXJOZuw+cZF9+ER82C6Stc0Up8JLCAqLnz6VtQWOMXoPR2VU0Bv56He+FBTL1cALfnj7LE8G+V9QPk1XyeGwCy8/m8HSwL4O9L/SJgwKzlV+SM/g28Sw/JqXTyP7cDOQOPw+mNvS5orYV6hc1TVVZLKX8WEo5Sko5Ukr5kZSySEpplVJeekh0g6EWArWw5ek1S4kKOH8wphECsyyNmquGmJgYOnbseNFxOzs7li5dyp49e9iwYQPPPvts+cbgsWPHeOyxxzh06BBubm4sXrwYsMlL79q1i/3799OiRQtmzZpFqsHEW88/yyNTpxK9axd+fn4V2vhjyWJ+37yNH1f+xUcvv4Sf7vo9bFVCXDSqFULgUrpPcKN6Y10JWSYzY/ed4EB+MT+0CmFiAy/aODtUeFm2/Evxjo1s+nU2Pzx2P9sW/obZaKxQz0hfd/p5ODMzKR3jFQgiFpgtPHjoFMvP5vBG4wa82Mj/on60cXagm7sT37YKYWuXFtzt70mwvZ5gez1W4N1TKZwpMVbblkL9p0ZPDyFEU+BdoCVgV3ZcStmojvp1WVQ1cr+QIouFY4W2oGhPnYZAu3PLKSUWK0eKSlBjW+++kvVtKSUvv/wymzZtQqVScebMGdLS0gAIDQ2lXbt2AHTs2JH4+Higorx0XkEBt/S7ld5GM/t37mDd8mUATJw4kRdffLG8jbdefZV1GzciVCrOpiSTl5GOw3nG4kbARaMmy2ShwGzBpZ7MCj6LTyOusIS54aH083S56LzFbObAP6sJaduBbuMmsHPZH2xf9BvSaqH7+IkVyj4Y6M3dB06yKj2XUb7uNWo/y2RmVlI6PyZlkGu28G5YIPcFeFV7XaiDnv8LCyx/f7rESLcdsXwan8ZHzYNq1LZC/aWmXkM/Ad8CZmxLQT8Dv9RVp+oSB7Ua19IlFJcLllLs1CqaOuhxVKtJM5iJLSwmucSIqZIRW6tWrdi9e/dFx+fNm0d6ejq7d+9m3759+Pr6lktR6/XnpuVqtRqz2RaANXnyZL748ksW79jFAy9Mx1BiIMhOhwoqDciaN28eGRkZLN2ynT+27qjQxo2Es0aNRiXIKA00u9mRUvJ3Ri69PZwrNQIAJ6J3UJCdRbuBQ/Bv2oyRz79Gow6dOPDv35hNFbfT+ng4E2qv46czNYvN3JCZR8T2w3wSn0Y3NydWdwyrkRGojCA7HZMaeDI/NZNTRTeNWoxCHVFTQ2AvpfwXm0hdgpRyBjdKUporwN9Oi7dOg1MlSVgc1GpCHfSEOdrholGTbjQTW1hCUomxQkBav379MBgM/PDDD+XHdu3aRUJCAj4+Pmi1WjZs2EBCQkK1/cnPz8fD15e8EgP/LlqIq1aNh05D9+7dyzeiz5edzs3NxcfHhyAnB05t30ZiDdq4HqiEwFOrJt9srVTe42bjWJGBhBIj/S9hBAD2/b0KF28fQtuf0/lqP3AoRbk5HNu5tUJZlRDc28CLqNxCDpUK/10Ks1Xy6rEzNNBriezcjJ/CQ2nv4lDlNdXxZLAvOiH4MD71qupRuPmpqSEoEUKogGNCiMeFEKOAKneZhBCzhRBnhRAx5x3zEEKsE0IcK/1Zs/lwLaNXqWhQjTqmvVpFsL2e5k52uGs1ZJnMxBWWkFhsoMRiRQjB0qVLWbduHY0bN6ZVq1bMmDGD22+/nejoaCIiIpg3bx7Nmzevtj9vvfUWvbp1Y+rIYbRsca78559/ztdff02nTp3Izc0tPz5hwgSio6Pp17ULK//4vUZtXC88tRoQkGm8+WcFazNs38GlDEFmUiKnDx+kbf/bUanOzTaD27THzc+ffX+vuuiaO/09sFcJfkqqelawMC2LE8UGXmnkf5En0pXio9fyYKA3S9Oyia3GECnUc6SU1b6AToATEIhtmWgJ0LWaa3oBHYCY8459AEwv/X068H5N2u/YsaO8kMOHD190rC4xWCwyqdgg9+cVyn25hTKlxHBRmbQSo0wsKrmi+tNKjHJfbqE0WaxX29UbjlNFJfJgXqE0Wy/92a7193kljNh9VN4aFXfJ8//8+I38dMJIWZibc9G56JVL5Ud3DJFpp05cdO7p2AQZErlf5hhNldZbYrHIDltj5MBdR6S1int4JWQZTbLppv3y1qg4uTkrr2L9x/+VcsEkKWu5TYVrBxAta/CMranX0C4pZYGUMklKeZ+UcrSUckc112wCLnRuHgHMLf19LjCyJu3fCOhUKgLsdLRwtMdNa9tDOF+xtMBsIaVUgtlyBV4yRilRC+pl8JWXVoNFQo7p5o00zjKZicotvORswFhcxOFN62nWtQcOLhe7cbbqfRsanZ59ay+eFdwX4EWx1crPyZVHG/+SnMkZg4mXGvnXuoifu1bDJ80actZoYuy+EwzZc4y1Gbm2gdvJSDi8DCyKZ1F9p0pDIIT4rPTnCiHEnxe+rqA9XyllCkDpz0suLwkhHhZCRAshotPT06+gqbpBqxIE2unQqwSnS4ylqp+SxBIj4rx4hMvFaLVWiECtTziqVejVgkyT+abV2NmQmYcV6O9VuSHYt/YvjMXFtBtUeQZXOycnWvToTeyWSEoKK3pchzs70N3NiXdOpjBm73E2ZeWX36dCi4XPE2ybw73cnWr1M5Ux1MeNqK4teT8skHSjmUkHT3HrriMsM3thQQWmojppV+HGoTqfvjLPoI/quiMXIqWciU3kjoiIiBvq6aEWgob2Oo4VGThjMCKwBfc0ctBzsshAocVy2ZLWRqss1xKqbwgh8NJqOFNiy2bmrdOgvQGMXlFuDqu++JAGzVrQbdyEKkfbazPz8NZpaOd88QatoaiQXcsXEdo+Av8mzS5ZR7uBQzm4fi2HN62nw+DhFc793CaUX5Mz+TYxnTv2n8BDq0Zbmg8622xhduvanw2cj51axb0BXtzt78mys9l8kZDGVLtefBrxI78WFBBkf1228xSuEVUaAinlbiGEGnhISnlPLbSXJoTwl1KmCCH8gbO1UOd1wUGtxk+nJdVgcwn00Wtw1qixU4vLzs4lpcQoJS43iW7TleCh1VBosZJuNJNhMuOp1eCn19ZY16m2yc/KYOFbr5KTkkxizH5KCvLpN3kKohIDZbJKNmTlMcTbrVIHg+iVSykpLLgoTuBCfEIa4REQxKm90RcZAke1milBPkwO8GJhajb78s6NwsMc9XRydbzCT3p5aFWCcX4ejPF1Z+XSN3nO+VZGHslikaMPoZXoIuWbLURm5TPAy+WKlG8VbgyqjfKRUlqEEN5CCJ2U8moXC/8E7gXeK/25/Crru6746DTkWywgwbdUyM5RrSa7dAmkpiM4s5TlqSLrKyohCLbX46uzkm40kWE0oxI2uYVrTe7ZVBa+9QrF+Xnc8cb/cWJ3FNErlmAyGBgw5YkKHj8AO3MLyDNbGVDJ/kBRXi67Vy0nrGsPfEMbV9t2w9ZtiYlch8VsQq25WPdHr1JxTwNP7mngeeUfsBZQCcHwvGhCTq7kzs6zGLn3GH+0a0IzR1s8aZbJzA+n05l9xhbY9myIL8+H+l/XPitcOTUN94wHtpbuCxSWHZRSfnKpC4QQ84E+gJcQIglbusv3gD+EEA8AicC4K+v2jYEQgsal2i1lD31HtYpMoy1XgEMN9f3LErjU1z2C87FTqwiy12OUJeSYLPjprm0WsxO7o1j3w1dYjEbGvfoOfk3CCGjeCq1ez/ZF8zEbDAx+/FnUpQJ6FimZcyYDnRD0cne+qL6oZQsxGwx0u2NCjdpvGN6WfX+vJOXoEQJbtq7Vz1brGPJpU3CMJQ1M3JGmof+uIziWLl8WWqwYpeR2L1fyLRa+O53O/QHeeF5HqROFK6em31py6UsFXPzfUAlSyrsucerWGrZ5w5OWlsbTTz/Njh07cHd3R6fT8cxzzxHWfzCFlpobAmPpxqCuHi8NXYibRkNSifGyDObVUJiTzYY5MzmyfTOegQ0Z8vILeDcMAWxGvNu4CWj1dmya9xNmk5GhT74IGi1PxiWyMj2Xh/Rm7C/4ejKTTrNv7Spa9u6HZ0DNZBqCWoYjhIqEmP03viEw2ja1m6uKWNGhDbPPZJRnxbNTqbjD353mjvYcLSyhT1QcXyamMaNJwPXsscIVUq0hKN0jaFpLewT1BiklI0eO5N577+W3334DICEhgT///JNWKlGt55DFYkFd+gA0ls8I/juGwFWj5oyAbJOlTg2BlJKYyHVs+mU2JkMJ3e6YQOcRYytdluk0fAwavZ71s79j4UfvsKzXCDaaVPTasRa3fZv4ydefTiPG4Nc4jOiVS4nbuhGtXs8tYy415rkYO0cnfBs15vSh/UDNZhHXDUOpd5OpmGB7PW9e4iEf5mjHWD935pzJYEqQ93VZ7lO4Oq71HkGdkPp//4chtnoZ6stB36I5fi+/fMnz69evR6fTVZChDg4O5oknnuBkQRFvTp/OwW1bMBgMPPbYY0yZMoXIyEjefPNN/P392bdvH9988w1vvPEGzl7exOzfz51jxxAeHs7nn39OcXExy5Yto3HjxqxYsYK3334bo9GIp6cn8+bNw9fXlxkzZpCYmMjJkydJTEzkqaeeYtq0abz22mt4eXnx5JNPAvDKK6/g6+vLtGnTavUeXQ0alcBZrSanNMn9hRSYLTwem0CovZ43ajjKzFu9muRXXi1/X2yv51DzUNIKcglo3or+Dz9e7ci9/cChWHV2PJ6YzSmTikF7NvB4eFNc+nUnatki1s38CgCt3o6OQ0bScchInNw9LuOT2/YJolcuxVhSfJFE9Q2FId/2swbuo8+G+LE0LYdP49P4oJkiYnezUWd7BPWdQ4cO0aFDh0rPLft5Lg4uLmzduRNpMtG9e3cGDLCldo6KiiImJobQ0FAiIyPZv38/f+3ei6u7BwPCW/Lggw8SFRXF559/zpdffslnn31Gjx492LFjB0IIZs2axQcffMDHH38MQFxcHBs2bCA/P59mzZrxyCOP8MADDzB69GiefPJJrFYrv//+O1FRUdfs3tQUN62avGLLRV5WOSYzd+0/yd78IuxUgmdC/Grkjqtt2BD38eOxSiuH05I4mJyAyM2mdVoO4U21uLtV/8AuMFt4xy2EeDx5mjyenfY4Gq1t9tC0czcSDu4j68xpWvTog73zpTWHqiKodVuili/iTOyhCppENxRWK5hK/9VN1ctPBNvrmdDAk1+TM5jQwPOiHA0KNzZ1tkdwLalq5H6teOyxx9iyZQs6nY7Ahg3Zs/8AG/9chloIcnNzOXbsGDqdjs6dOxMaGlp+XadOnXDz9cNJraJx48blBiM8PJwNGzYAkJSUxPjx40lJScFoNFa4fsiQIej1evR6PT4+PqSlpRESEoKnpyd79+4lLS2N9u3b4+l5fb1QKsNFo0YlIPu8CO10o4k795/gWKGBJ4N9+TwhjTUZuYzzq/4hbt+qFbl2OtZ9/wXpyfE07dyNHoNGYFy8mOzf5qMLDMTrvBnchZyfcOarFsGMuaBNIQQhbdoT0qZ9pden5xvwdq4+9WRAsxaoNRoSYvbfuIbAeF7QWw0Dyp4O9mV5WjYDo49yu5cr04J9aXeVwngK14YaGQIp5Zt13ZGbjVatWpUnlgH4+uuvycjIICIigoYNG/LShx8xdPDgCvkOIiMjcXSs6A+u0+sxWSU6rQqVSlUuVa1Sqcplqp944gmeeeYZhg8fTmRkJDNmzCi//lLS1g8++CBz5swhNTWV+++/v9Y/f22gFgIXjZpckxlR6p3zZUIaWSYzP7cJpbe7M4vTsliall0jQ3B48wbWfP0pDm5uDH/2ZZp27mY70aoVJXFHyFu16pKGwGSVTDhwsjzhzO3ebpf1Wf6NTePx3/by2Z3tGNiq6rwQWr0dDcJakBiz/7LauKaULQtBjWYEAL56LVu6tGBWks2t9K+MXKaH+vFUyI2VJ0PhYmrkr1i6R/ChEOIvIcT6slddd+5Gpl+/fpSUlPDtt9+WHysqso2cBg4cyOLZs8gpzRFw9OhRCgsLK62nTEqgqo3i3NxcAgJs6+Rz5869ZLnzGTVqFGvWrGHXrl0MHDiwRtdcD9w0aiwSUgwmph9Nwk+vZVG7JvTxcEEIwUgfdzZl59dIvTQ4vB3tBw3lvk++PWcESnEZOgTDseOUHDla6bWfJ6QRnVfEly0aXrYRAGjf0J0wP2ce+XU3v0cllh+3SisrTqwgrTCtQvmGrduSHn+SorzcC6u6MagwI6i5MqmnTsOLjfyJvqUlw33c+OBUKtG5lf/tK9w41NRxfR4QB4QCb2LbM9hVR326KRBCsGzZMjZu3EhoaCidO3fm3nvv5f333+fBBx+kZYuWjOp+C61at2bKlCnlI/ULKVsdr8p1dMaMGYwbN46ePXvi5VWzRCQ6nY6+fftyxx13lHsn3YiURWPrVILF7RqzskNTOp4XRTvK1x2zhJXpOdXW5ejmTt/JD6N3uDgK12XQIFCryVt1sejbntxCPk1IZayvOyNrmCnsQjwcdcx/qAs9mnozfclBvlp/DCklv8f9zstbXmbi6omczjtdXr5heFsATh86eEXt1TmGy18aOh9njZqPmwXRwE7L47EJFJpvXsHB/wKiJiJgQojdUsqOQogDUso2pcc2Sil713kPsWkNRUdHVzgWGxtLixYtrkXzV4TBYiWusAR/vRYf/cWuimVkGE2cKTHRwsmuVgPKrFYrHTp0YOHChTRt2rTW6q0rLvV9SinpFRWHp1bDsg5X9zkSH3wIY3w8jdetLQ9iK7RY6L/rKAarlQ2dm1+Ute5yMZqtvLBoP8v2JTO6s4ZNRa/QwqMF8XnxaFVaZg2YRSO3RljMZr558C6adevFgIefuKo264QTG+CXkbbfO0+B2z+4omq25xQweu9xJvh78n9hASxOy+abxLN46TTMDW901fdboWpKn93VbkTV9MlTlmMvRQgxRAjRHltuAoVLoFersFeryKlmJGS0SoQAbS0Gkx0+fJgmTZpw66233hRGoCqEEIzydWdnbuFVJ1p3GTIEU1ISJftta/NSSmYcT+ZUsYEvWjSslYeSTqPikzvaMbl7EGvOforFouW9nh8ye+BsrNLKfX/fx7bkbajUakLbRXAsajuWS8wWrytXsFlcGbe4OfFoQx9+Tcmk0/bDPB13GrUQROcWMXbfcbLqSRrTm52aGoK3hRCuwLPAc8As4Ok661U9wV2rpthipaSK4DKjlOiEqFWZhZYtW3Ly5MlyF9ObnVE+7khg+dmcq6rHuf9tCJ2O3JWrbEbgRDK/JGfyWEMfulciH3GlqFQCz4CNqO3PkJ80khcXJODvEMrcwXOx19gzZd0U7vnrHkxhHpTk55F4cF+ttV1rlG0WC/Vl7RFUxguhfnR1dSTUXs+8No3Y0MmWavNoYQmj9x7nrMFUfSUKdUpNE9OslFLmSiljpJR9pZQdpZRXko/gP4Vb6QizqlmB0Sr/UxHFV0Kog562zvYsTsu6qnwGaicnnPr0Iefvv3khLpHvT6fzQIAXLzeqPbG0+Nx4Xt/6OrNiZjGi8Qj+b+DdbD+Zyd0/7MBR5cfykct5retrZJZkMiP1Cyw6FYe23IB+F2V7BI7eYC65qqr0KhXLOjRlWYem3OppcwK4zdOFX9s0IrHEyG3RR/g28SyFZgtSSrZk5zNu33Habo1RZgzXiCrdR4UQX1R1Xkp544Sq3oBoVSocNSpyTGZ8dZpKR/1GqxUHrSLUVR13+3vy4tEkducVEXEVkszOQ4bwil8j/k7N5vGGPrxSS1m/jmQdYdbBWaxNWItWpWV8s/E81eEpHLQOeDjoeOy3PYz7bjs/39+ZO5rdwaimo5gfO5+th2bDjs30vO8hXJ1uIM1/Y+mMwNG7zhLT9HB3Zmn7Jrx9Ipk3TyTzRUIaQfY6DuQX46XVkGEy83tKFo82rDI9ukItUN2MYCrQA1swWTSw+4KXQjW4a9QYrJJi68XLQ7bsZv8tsbkrZayvO85qFT+dqTrJe3UsaRbO37f05v4dkbwU6HnVRmDf2X08/u/jjF0xls1nNjO51WTWjFnDy11exkFrC6a6raUv8x7sQmaBgTHfbiM2JQ+tSsukVpPoN2A8arPklZ+nkmfMu6q+1CqGfFBpwd7tqpeGqqKtswML2zVhVYemdHZzxGiVvBcWSPQtLeni6sjcMxlYb9KsdjcT1RkCf2xZwgYCEwEt8KeUcq6UsmYO7f9xXDUaEJXn6zVKm3FQloaqx1Gj5g4/D1aczSHdeGVryieKSvhfwll6YuKeud+T+eWXV9WnZceXMXH1RPan7+fxdo/z95i/ebrj03jZX+ziGxHiwcKp3RAC7vh+OztP2vITj+v/MBpnB9RHMpi2fhoW6w3iZmkoAL0TaB2uSarKjq6OzA1vRGTn5kwO8MJOreK+AC8SSoysz8qvvgKFq6JKQyClzJRSfiel7AtMBtyAQ0KIqlMx/UdwcrLlkI2Pjy9XIL2Q88XVTp06RevW56SHC8w2Q+BQT1NU1jaTA7wwSslvyVmXfa3JKnnscCJ6lYovu7XD/Y47yPxxNkW7riwcpsRcwpd7vqSNdxv+HvM3U9pOwVV/cdJ6Ka0kJP5ASUkyzfycWfxIN7yd9UycHcXfh1JRqdSE97iV4AwnDiTtYc6hOVfUn1rHWAA6Z9Da1+mMoCpu93bFR6fhp6Rzs8Ask5kvE9LIUfYOapWaRhZ3AJ4C7gFWoywLVaAqQwA2cTVTJctDBRYLepX4TySkqQ2aOtrR092Jn5MzMJdKd0spyTCaSTeaLn4VFpT//nF8Kvvyi/igWRB+ei2+L76AtmEQyS9Ox1JQUE3LF7PgyALOFp8t3wcAsFovfjilpa3k+PH3OHnqcwAC3R1YNLUbLfxdyqOQm3frjTRbuN3cia/2fUVsZuxV3KVawpAPeufSGcH1MQS60mxt67PySCg2kG40MXrvcd45mcLzR5KuynFAoSLVbRa/CQwFYoHfgZeklDecKd78x1EyTl/+P3NVeAU50fOOsBqVnT59OrGxsbRr1457772XUaNGMXHixHJZic+++BLXNu0pOM97qGfPnkx77wNu6dgRgO7du/Ptt9/Spk2bWv0c9Y37Ary4PyaevzNt0gyfJ6RxIL9mD6qxvu4M93EDQOXoSMD77xN/9wTS3n6HBu+9W+M+FBgLmHVwFt0adKOTXycAMjIjOXToKcJbf42HR3cArFYTJ099CkBa2gqaNpmOVuuOh6OO3x7swiPz9jB9yUGeGxCGq48vARk+bPNw56XNL7Fg2AL06uoF7OoMQ37p0pD9NVkauhQTG3jyeUIaH55KZW9eEckGE6N93VmSls3iNBfG1kCDSqF6qnNXeQ04CbQtff1f6eaaAGRZlPF/nffee4+PPvqIlStXAjbNoXXr1mFnZ8exY8e46667+H3jFjIs50YwE++7n2XzfqV/504cPXoUg8GgGIEaMMDTlQC9limH4jFLCLHX8Wojf5zODwY7/i/ErQL3ECg8C8Yi7D2CGdb2oQp12bdrh9fUKWR88y1OffviMnBAte0XFp5gxcH3yDFk80R7W0SwlBaOH38fszmfw7Ev0KXzX2i1rqSkLKK4OJHGjZ7lxMmPSU5ZRHBDWx8c9RpmTYrghUX7+WjtUaZ4hFEQu50Zd73KY1ue5Is9X/B8p+dr7b5dNoZ8sHe/rktDYMtpPdjLlUVp2TirVfzethERro4klRh56WgSXd2cKgg7KlwZ1RmC0GrO3xDUdOR+rTCZTDz++OPs27cPtVrN0aNHcdGoMVislJmC20aN5u2330ZvtTJ79mwmT558Pbt806BRCZ4L9eO35CzuD/RimLcbmrLNdilhwzuw6UNoPRZGvQZmA+yZCxvehbwouO8vOC85vdcjj1CwaTOpb7yBfft2aH0u7aoopeRAzFP4FB3m/qBwWnvZ9nvS0lZSWHiU4IZTSDz9I0eOvE6LFu9zKv4rXF3aExz8CJmZmziT9BsNgx5ACNtSYFkUsqeTnpXrzjDabMb7rJ6xYWOZFzuP8c3G09ClYd3dzKowFoBbw3ObxVLCdfJuezLYl1SDibebBpbLWn/ZoiH9dh1hWmwii9o1RqV43l0V1W0WJ5S9Sg81Lf39LHD5O3b/ET799FN8fX3Zv38/0dHRGI3GcvmCMlc4q05P9363smrFn/zxxx/cfffd17PLNxV3+XuyomNTRvm6nzMCALvn2IxA+4kweiaotbbljVsegyEfw+kdsPXzCnUJrZYGH3yAtaSElFderXLdOeXsaooKD5NrEbRVHaWo6JRt+efkZzg5taBx4+cIDZ1G2tmVHDgwBYMhlcaNn0MIQWDgPRSXJJKZubFCnSqV4NUhLZgwvA8GlY5f/viLyS2moFVp+Xrf17V52y6Pcq+h0gxqVxlUdjWEOzuwsmNYhdwGwfZ63m4awLacAobsPsaa9FzFzfQqqOlm8UPAIuD70kOBwLI66tNNh7OzM/n551zccnNz8ff3R6VS8csvv2Ap3xQWWCRYpKTIauXe++9n2rRpdOrUCQ8PZa3zqgkfB4Peg2FfVBj1A9DmDmg5EvP6D9nx63aORadhLd1w1jcKxef55yjcvJns+fMrrbrIVMCOmOmcNQmM/s+gVtlx6PCzJCcvoLjEtvwjhIrghlNwdWlPVvYWPNy74+7eFQBv7wHodN4knfn1orqFEDx6axhuYeHYpx3liV+OMbrJnaw+tZqj2ZXLZtc5hnzQu9hmBHBdl4cuxZ1+HnzSLIhMk5nJMafot+sIhwpuvH7eDNTUXeUxoDuQByClPAZccbifEOJpIcQhIUSMEGK+EMLuSuu6EWjTpg0ajYa2bdvy6aef8uijjzJ37ly6du3K0aNHcXR0RAiBk0aFFUmB2YKU0K1zJ1xcXLjvvvuu90eoH+idoOsjUJkXlhCY+n/EqpxX2b2lmLWzDvHbjB0c3pqMxWLF/e67cezRg7MffIjh1KkKlxaaCvlow3hcRSFOfhO4s/WjNG/2Fnl5+zl67H+4unbA07MPACqVhpYtP8bDvQdNmp7LnKdS6QhocCeZmRspLk6kMvre1gcHSzFZCSdYs7U5DhpHvtr7Va3dnhojZan7qBNoS/81b0BDIITg7gaebOvSgq9aNCTDaOaFI6cVb6IroKaGwHB+4nohhAa4orsthAgApgERUsrWgBq480rqut4UlLodarVa/v33X/bv38/TTz9N06ZNOXDgADt27ODdd98tL9eqcWMW74gmxWhCCMhNS8VqtZanp1SoO4zFZlb8mMiZkub0c/mCQUFz0Vnz2PBLHJt/P4oQAv933kGl15P8wotIky1oTUrJ9E3P04wjWLUBDG3zBgC+vkPw8x2JlJbS2cC5JSoHh2Dat5+Ls1PzCn1oEHAnQqg5daryQLaQdh0RQsWTTY1k52swZPZkw+kNHEg/UEd35RIYCwF5LqAMbkhDUIZGJRjr58H0Rv7szitiXeYNFKF9k1BTQ7BRCPEyYC+E6A8sBFZcRbua0ro0gAM2CYt6j6NahUqAwSL5+/f5dOvalXfeeQeVEkdQp8THpvLdh/M4E5/GgAfDaXH/QzT2PME49R2Eu23i8JYzZKcWovX1we/NNyk5eJCjv0/jYMwTrN4xipaGdXhpJO1bvFm+0QvQosX/0SliWfnyT3XY6f0IbvgQKalLSE9fe9F5e2cXGjRrTvHJGBZO7Ya2sBdYHHl189sVktrUOWXKo3rnc3sE19GFtKaM9/Mg1F7HuydTlP2Cy6SmT6DpQDpwEJgC/AW8eiUNSinPAB8BiUAKkCulvOi/QgjxsBAiWggRnZ6efiVN3XCohMC5dNN44qRJnD59mnHjxl3nXtVfko5ks+iT7fzy689kWU9Bk3hC23lC8yEwZTNiwkIiGmxHTQlRv24CwHngAExPNyUpYC2ZZ7eTm38YP70dvr4jy5d/ylCp9Li4hF9Wn0JDp+Hs1IrYuFcwGC7+u27UoTPp8Sfx15SwZGo/nIvGcDL3GEOWDmX65ukcyz52xfejxpTlItCdbwhu3BlBGVqV4IVQf2ILS65asvy/Rk1lqK1Syh+klOOklGNLf7/SpSF3YAQ219QGgKMQ4p5K2pwppYyQUkZ4e3tfSVM3JGXeQ86KrESdIaVk54qTLPl8B3E5m0FvpGuXW8jMOcvGjaVeO0JA2AAcpiymXYP9HD+uJ33tPE6c/Ij0podw3O+I2zNFnP3Nga6N5tO61ce1olKqUulo2epjLJZCYuNeumg9u3HHzgCc2ruLQHcHVt73JMHF72DM7M7aU/8y+s/RPLn+SWIyYq66L5fEULq0UmFp6MafEQCM8HGjhaMdH5xKwWRVZgU1pbrI4oNUsRdwhQFltwGnpJTppW0sAboBF7tT1EPcNWr0DnoclRR9tU58fDzbt28nK7mQ3PRi8C9CpbYwYcJEgoODKS4pYvPmzTRt2pSgoCDbRXon2k17goMvb2RP4gI0mkMEBNzNTrMTxU3m0j/KRO7wu9E89BDe02onpaSTY1OaNH6Bo8fe4viJ9wgNeRyNxpYYxyMgCFdfP07sjqJt/9vxcNTxxwMDeGSeF5uO9KFPxBF2pa1m/ar1tPNuh7udTbpap9YxrNEwegX2unqDVZaLQH9zzQjANut+qZE/kw6e4u2TyTwf4lcebJhlMjM7KQO9SvB4Q59aTQZ1s1NdQNnQ0p+Plf78pfTnBOBKhwiJQFchhANQDNyKTeL6P4EQQjECdYTRYCQpPo2SAhP2rlrcfbwYNGgQAQEBAAwePJj4+HiWLFnC1KlT0ettEg46FweajViNUXMItwR7PNHydfpiejw+iPubPMvZ998j45tvcB0+DF1IyGX3qyC7BHsXHerzZoGBgZPIyztIYuIskpMXEBgwkYYN70erdadJRBf2/b2KkoIC7JycyqOQn1+kZflORybcMojGrQ+yNmEtyQW27bXMkkz+jv+bMPcwHgx/kIEhA1EJFck5xfg469Fczgy0fGnI6abaIyijv6cLI33c+P50OgtSsngg0IsCi5VfkjMpKs0WeLrEyHthgUogWilVGoKyQDIhRHcpZffzTk0XQmwF/ne5DUopdwohFgF7ADOwF5vUtYLCVWFIccAhoQ09Bjak68jGF4347OzsGDVqFHPmzOHrr7+mW7dutGsXzuHDz2PU/E1iQht2nuqIyPkNo4eGJ9tPQ+cagO9rr5G/IZLs+b/j+9L0y+rT4a3JbPg1DmcPOzoMaEjzbv5otGqEUNGq1ccENbyP+PhviU/4luycKCI6LqB5t97sXrWcY1HbCO9n8yjTaVR8ekc7PB31zN56imFF7fl53GR0GtsD3mQ1sfrUamYdnMULm15gadw6zGl3sD4uk55NvZg5MQJ7XQ0HIOdvFqu1tt9vkhkB2AZb37UK4eHAQj5PTOPj+DTUwpby9IlgXxamZvFV4lmKrVY+bdawYlDif5SapsZyFEL0kFJuARBCdAOuOE2UlPIN4I0rvf5GwcnJqdw1VOH607JHA/SOGppG+F5y2h8SEsKkSZOIjIxk06b5JCe/iKtbEgnxHRDGkZg4SUn+SEaqVhC86GGKNG7syvUgrU9HQlctw9tvJyr/5pj7vsH+mEPExcWVr/PrdDqGDh2Kg4NtXf3AhiQ2LzhKgFcWZjs/Ns4/yq6/4rnt3pYEtbQFELo4t6ZN+NckJs7m2PF3yMuPwbdxK9x8/YnburHcEIAtCvm1oS3wdtbz/po4coqMfHtPR5z0GrQqLcMbD6eLd3/uWfR/bD+7DIrSGNvxGZbsSeXen6KYPbkTTvoa/MufbwjKvKRuIkNQRofSHAenigxoVaJck+iVRv44qFV8cCqVLKOF15s0oJnjTR3KdNXU1BA8AMwuTWAPkAPcXyc9UlC4QizWbHyaJpGbm1RlOVfXQjpG7CQj4x+k1GG13Mm4cdNxsHPk85cXIx1icSgczRrjGfYU+WGUGtSeFg7fGszRo6kEJ0Sxc+/75BskHh4e2Nvblk9OnDiBm5sbAwYMYM/fCWxfeoLQ5noGZk9B1fUpzgQ+xqbfj7J29iHufK0zjq7n1EX9/cdy4uQnJCX9SssW79G8ey92Ll1IYU42jm7nUlgKIXikT2M8nXS8tOQgd/+wg58md8LTSU9KbjETfthJSm53+nXxYhOzKHSfxYd3vMALC2O5Z9ZO5t7XGVcHbdU38vylobItwhtoaciSb0TtXHOhuVCHiiquQgieCfHDRaPmnRMp9I6K43YvV54O8SXc2eEStdRvamQIpJS7gbZCCBdASClz67Zbl8eGOTM5m3CyVuv0CW5E38kPV1uuoKCAESNGkJ2djclk4u2332bEiBHEx8czaNAgunTpwt69ewkLC+Pnn3/GwcGB//3vf6xYsYLi4mK6devG999/jxCCPn360KVLFzZs2EBOTg4//vgjPXv2rNXPVZ/JytrKocNP16isRuNKaOiTBAVOQqt1A+BU7il2+Kyle8JwigMOs7MoiFatWtGjRw+cnJz4+9nniPPx4Rj+BBtOM+L2cTTuNKB89rF06VKioqLQ5zUg5p+zNI3w4daOsaiXmyHtAIG3eTBoSjh//N8uNvwSx5DH2pRfq9W64Oc3gtTUpTRtMp3m3XuzY8kCjmzfTIfBwy/q/x0RQRVyIb89qjUvLDpAbpGJnx/oTKeQQSw6GsD/tv8Po+VtPrvzVZ5dEMebKw/xyR3tqr45hnzbTEBrb4syhhtmRlAYnUb2oqN4TmyJfSvPq6rrwUBvRvm4MyspndlnMvh7dy5ftwhmpO8NlDv6GlEjQyCEeP2C9wBIKS97j6C+YWdnx9KlS3FxcSEjI4OuXbsyfLjtH/fIkSP8+OOPdO/enfvvv59vvvmG5557jscff5zXX7fd0okTJ7Jy5UqGDRsGgNlsJioqir/++os333yTf/7557p9tpsNd/eutGtXfQZVgcDFpS0ajVP5MYvVwsfRH5Pgf5Ch+ZPxMN7CkCfC8fA491Do17cPTd75Pzw+fZ+g/Qsgaj+064kUWnJXraLZps0ccHVle9RWOnfrRZ97mqNav8h2cYotOtjD35FuoxuzecExDm1OpnWvgPL6AwMnkpz8e6lc9YN4B4cSt3VjuSHIzNxEYuKPlKUE8QC+v91MXGoeW6Ps0HIH8x4aTJtANwDGho3FTmPHq1texWB5jU+D3Fl4IJCM21vg5aTHWmym5Fg2JUeysWSfJyqXGQ6md+GHg7b3xnchKgCOXWaEs0rg2MUPh/Dacf82ZxaT8+cJAAq2nrlqQwDgqdPwYiN/pgZ5MznmFI8cTqDIauVu/6uv+2aipktDhef9bse5ZDU3BDUZudcVUkpefvllNm3ahEql4syZM6SlpQEQFBRE9+62PfZ77rmHL774gueee44NGzbwwQcfUFRURFZWFq1atSo3BKNHjwagY8eOxMfHX5fPdLOi1nqwr8DArQ1vvazrTFYTL21+iY1JG3mh0wt0bd6EtbMOcfZoCR7nBQ27DhuG3Ucfw+IVFPZ/Eta9huGNe8namYUhJZVjTe9A38wPg2Mq7Yb4olIJOFv6b1KQCgVnwcmH8N6BxB/IYOuiYwQ2c8fN17Yc4ezUHFfXCM6cmUfDoPtp3r03m3+bQ05aKgaxl0OHnkav98FO36C8T856QasGThQVHKJryGLCA+6o8NmGNhqKndqO5zc9z1xRxERVB/YsiKWdSWBMzAMrYKcGL/vyjWesVhA6KPPDF1qwyHPva4g110DWb3HIURYcO/td1rXSYsWcUYzGxwEhBNIqyfrjKAhw7OpP4Y4UTGmFaH2veKuyAq5aDfPaNOaBmFM8E3eaYouVBwLrT/xSddR0aejj898LIT4C/qyTHt1kzJs3j/T0dHbv3o1WqyUkJISSEtvo6sINSyEEJSUlPProo0RHRxMUFMSMGTPKywPlLo1qtRqz+YZLBndDs+joIt7Z+Q6f9vmU24Jvq9E1BouB5yKfIzIpkmc7PsvElhORVsnehoms/zmO07FZdBwYgkcDR1SOjriNGU3W3J8p2LAB8MKiiiOz9QDi2w+hoBCaJW4iJszCv38uZ9x990HaYXD2h/wUSD0ATW5DqAT9JrXk97d2smPZCQZNORedHBh4D4cOPUVm1iaad+vF5t/mELPrMwwOi3F1bUe7trPLYw7OJyVlKYdjnyMhcRYhwVPKj1tLzHTLa8dvxW9hPC3xNLvBsXyMfnqcewchGrkyZvl+TpzJZljbBjzapwnNNn4IGcdh6g5bJR/fCU36wYjLk8WWJguZv8aSveQYVqMF5x4B1V9USvbS4xRFp6Fr6Ixzv4aYUgowJuThPr4Zdk3dKNyVSsH2FNxHNrmsPlWFg1rFnPBQph5K4JVjZyi2WHk82LfW6r+RqemM4EIcgEa12ZGbldzcXHx8fNBqtWzYsIGEhITyc4mJiWzfvp1bbrmF+fPn06NHj/KHvpeXFwUFBSxatIixY8der+7XK8aEjWHp8aW8uf1N2vm0w8veq8ryFquFpzY8xZYzW3ilyyvc2dymfShUgqGPt2Xv2gRiNidzdGcazbr40eeeZng/8wzOAwaA1cqZRAM7NmRTZHbE166A3vfegr9zKKa33uaQSkXnf/8mODcRuk2DbV/Yloea2AyUk7uesC5+HN6cjKHYjN7e9q/o4z2QYzov4mJfxs4+gFZ3plFiH4u7W1fatf0BtbryzUw/v5FkZPzLyZOf4ioj0CT4UByXjTEhD6wSF60TZnbwj3scsz2Pk68rYozraHIOdudYRhGjOwSwJiaV5fuSWeORQpi74znZgSvMUia0ajwntiRrfhy5K09StO8sojSeQd/EDZdbGyIqcd0sPpRJUXQads09MKUWkjnnEAD24V44tPNGCIFDG2+K9pzFdVAIKrsrfYxdjF6lYmarEKbFJvD2yRSKrFaeD/Gr98FnNc1HcFAIcaD0dQg4Anxe3XX1GbPZjF6vZ8KECURHRxMREcG8efNo3vyc4mSLFi2YO3cubdq0ISsri0ceeQQ3NzceeughwsPDGTlyJJ06dbqOn6J+oVVpebfHuxSbi3l96+vVyhH/fPjni4xAGQ4uOrqPbcq973Sjw8BgjuxM5a9vDmBBg0PHjqRqQ/hngwk7H2+GR0QyRjORkLNfow8JYdArL2NnMrF05Rpyz9pBcDdbtq/UgxXaCOvki8Vs5dS+c5pDKpWOpk1ewdGxCWqVPU7ufmQccseUcDtqtQPSKrEaLFjyjZgzijEmF2A4lUtxTAZ+x+9DbXAk5sDTZP99FFlixrlXAN5T2tAgYhnBmneZXLwUz4xxuFpuYdHRxazJe4rWbf/i6cGebH2xH4/2aUxxQQ6HMqwUGUtnpFoHzIZCDiXnXrbEs9Co8Li7BU69A1HZaRBaFUhJ/r+JZC85hrxgucmSbyR7yTG0DRzxvKcFfs9H4D4uDIf2PriNbFL+QHa8xR9ptFC09+xl9acmaFWCr1oGc5e/B5/Ep/HmieR6L20tavIBhRDB5701A2nXMol9RESEjI6uGHwcGxtLixYtrlUXLmL//v089NBDREVFVXo+Pj6eoUOHEhNTh5ow9Yja/D7nxc7jvaj3eK3ra9zR7I5KyxzJOsKdq+6kT2AfPunzSbUjvrjtKaz/ORa/xq407+pP5G9H8Al2ZujjbbGzV8OqZ2D3TzD5LwjpzqGdUSxc/Rctjhxh8J3jcMhZhzXjDHLML0iDBavRgrXEQtTS4zg5amkW4Ws7brAgjbaX1WBBGizkn81AGi3Y2zmDWV5S9EXo1RhbneSk1xt4uPSiTftvUatL/eNn9oWsk1CSw6oWH/HY3ga4uxSh89iMxWk7HnburBy1Ep1aR95HHdia68mPDd7k87vao5o9gIQ8uLNkOs39nHm0bxOGhPujvsJALCklef8kkv9vIvZtvfG4IwyhViGlJHPuYUqOZ+P7RPsq1/+llJz9ah/SZMX36Q51MmK3Sslrx87w45kM3gsLZHJA1TPMGxEhxG4pZUS15aozBMKmu3ugNHfAdeFGMwTfffcdX3zxBZ999tklcwkohuDyqM3v0yqtPPLPI+xO201jt8YAaISG/sH9GddsHBqVhrtW3UV2STZLhi8p1+upjmPRafwz+zBWq6RBUzeGPNYGXemyhOFICjlz1yL1vlg17kijhU2WGI6okxlq7ICfrKYNAUKvQaVXIXRqhF6NqvSnVWXl2J7toBG0vLUfGns9Ql9WxlZeZa9B6+eI0Kg4c+Z34o68irtbF9q0mYlG6OHdAGgzHvbMJa3Hw/x0Nh4fh3Qa+zhhoYSEvAT8Hf3wsPOAtEMY1Y4cNbiTZ3SiVUoye1Twj3coGYXFGExW7NQO3Nf8Kabc0gOdRoXVaiUuLo69e/fSs2dPGjasPtdy/sbT5K6OR+1ph8pOgzRbMacV4Tq0UY32E8pcSb0eCseusVtNvsLLRkrJ3QdOsiOngH86NaOxQ+0GnuWYzMw+k8G+vCLu9PdgkJdrrcpe1JohKK1sHvCSlLLy1Ep1zI1mCBRqn9r+PtOL0vlk9yfkGW1KmjmGHA6kH8BF50Jzj+ZEpUbxza3f0DPw8uI0Eg9lkngoiy4jG6E9T7LBmJRP3pzFCEsuqta3I3QqTLF/8GtRCRYTDI3NICjsBOphbyACW6HSqxE6NTlZJSz8aA89xjclvG/QJduN37+Hxf/3Ou0GDqXffVOqHQGnpC4jNvYFXJzb0NBtMGL1dCxdHiIl+Q+yXCRWHJG6Nvg42x5sB9L3U2gqorN/Z9QnIsGlAekOwRQWHcRZXUSSUcUZQxhWB3vyDCaO5iVzvFjgmPUIE0OCIC2OjIwMAPz8/Hj44YdrlGejcHcaxQczyt9r/RxxGRBc6d7BhUiThZR3o9A3dsNzQt09C1INJvpGxRFsr2dFh6Zor3AmlFRi5GD+ucC86Lwi5pzJoNBixUurIcNkJszBjmdCfGstlqGmhqCmuyz+wCEhRBTnuZJKKS+OdFFQuAHwdvDm3Z7vVjh2IP0Asw7OYsPpDYxvNv6yjQBAw1aeNKzEf10X6IxXfwOsfh36DAWPRnB4JmPDRjL7qCt79dk47Y4jYNRBRPAt5dd5edjh1sCRY9FnqzQEIW070H7wMPauXsGZ2Bg6jxxH2C09UF2Ym7kUf7+RqFV2xBx6ioN5e6GVCxQsQOuopnGGE4GjN1b0Pjq7j4mrJ6IN6MaDMcvIvOVu3sqM42Q2THRyo6WdgUDnOFtZBxjoANvSQslLiSXjUALCwZUxY8ZgNptZvnw5sbGxtGrVqtr76djRF8eOV+aZI7RqHCL8KNiShCXXgNpVX/1FV4CfXssHzYJ46FA8nyWk8nyo/2Vdf6ywhC8S01iSlo3lvHG3Chju48a0YF/CHOxYkZ7D5wlpTD2cgL1axUAv10vWWdtUJ0PdBPAF3rzgVG/gTF11SkGhLmjj3YYv+n1BelE6nvZ1EDAUNhBWvwBH10DrsVCcRVCjZrSx9yUGCIuLw3n1Wly7VIx7adrJl53LT5KfVYKzx6WXHvpMehC/Rk3ZuWwhq774kG0L59FpxFha9uyLWnOxbISPzyC6u27CGDkDDi2FB9bisG0u6mO/gdqpQtl2Pu3oHdib2TE/0Uej5pmMjaRYDXx92/e4r5rPgmR3mqrSKHZzIdtgICR0L128Etma3pgdmkMcTRvN7c4BdGzoxrZt29iwYQMtWrSo8+x7Tl39KdicREFUKq79g6u/4AoZ5uPG2Ax3PktIY1V6zYUVrBKOFZVgpxI8EODNKF93tKUTCk+dBn/9OamMUb7uDPN2o1dUHO+dTKG/p8s1U0et7lv6DMiXUm48/4UtQ9nIuu6cgkJd4O3gjUrUwQPKPQS8W9gMwVmbyyM+LejTpw9SpeJo53BSlx7GlFwxM2vTCNuIOO6fo1VWr1KpadmrH5M/+prhz7yM1s6etd99waxpD7Fn9Z+YDCUXXaPX++Ccehpnp+Y4u4aj9moBpkKoRI/pifZPkG/KZ1yAH2ctRXzX4V38f93CspOOuGXn0jHyON2+WUmnqFTUqYPQah0Z0CWJdKczePoe4ukF+yg0Wujbty8ZGRkcOFD3uZY1HnbYNfOgMCoFabbWaVv/FxbIBH9PGjvoa/xq6qjnqWBfdt3Siv81DaC9iwOtnW2v841A+edRCV4I9bvmWdaqWxoKkVJe9G1KKaOFECF10yUFhZuYZoNg25fQsHT5x7cV7o7udOzYkd1WC2F2B0l84AEazpmD1tdmADQnD+KaH8/+Nbk0CTTh3q1jlU0IlYqmXbrRpPMtJOzfw46lf7Bhzkx2LFlAx9tH0G7gEPQOpR43UtriF1qOsL33bmb7mXEE3CouRTXzaMbwwL4cPLCOd4+GoPrwWdZ16YzZ358xlhUE/nOQgsiN2H//Hb5f/Yp4qCNn2m9nkn8wa3JPcOR0MW+tPMz7Y9rg7+/Phg0baN26NRpN7fn5V4bjLf6U/JRF8aFMHNrWXTSwi0bNB80uvXxXWwz3ceOLhDQ+OJXCUG+3K96TuByqGxZVtUVuX5sduRkRQjBx4sTy92azGW9vb4YOHVrFVZfmu+++4+eff66t7ilcD8IGgdUMu2aBozc42lwOe/XqhUqt4mTfMMxpaSRMuAdjUhIFmzdz+uGHaVm4BaPWiX8+3UjB9u01akoIQUi7jtz55vuMf/N9fBs1YcvvP/P9I5OZ/dTDzH7qYX566iEOpurBvzSZoFepIUi/ePYhpWTaDkfenylRbT7K2ZEjSA4I4LamWoIckxCAc7++hPz+O95PTkP+sBuXxCBaqRORxuNM6unKH9FJ9P0okr+zvcnNzeXrhRelI6917Jq6o/a0o2B7cvWFbwJUQjC9kT+nio38kZp1TdqszlTvEkI8JKX84fyDQogHgN11162bA0dHR2JiYiguLsbe3p5169aVZ8O6EqZOnVqLvVO4LgR2AnsPKMqE0N7lh52dnenSphlb91rpcp8rxXOiiB8zEkuhAX3TpjT58SPM61PY9Y+a6Fd/IHziEdRubgDoGgbj0KF91c02b0XgS2+SdvI4B/5dg7HYFgmcEx/L2jNhGI8W0bETNsNk726bEZyHtFpJe/c9sn9ZhktwMR4zPmX19ngCnJ3pEpIPJwBzCehs2j9ejzyCsLfH+tl75L+voq+jiYCQRB5RdeBMdjFIVwpOnaYobi+f/h3CUwNa1Fl0rlAJnLr4k/vXKUyphWj9akd/6HrS39OFji4OfByfyhhfd+zqOMd5dYbgKWCpEGIC5x78EYAOGFWH/bosclacwJhcWH3By0DXwBG3YY2rLTd48GBWrVrF2LFjmT9/PnfddRebN28GoLCwkCeeeIKDBw9iNpuZMWMGI0aMYNq0aXh5efH666/z999/88477xAZGcn//vc/nJyceO655zh+/DhTp04lPT0dtVrNwoULadSoES+88AKrV69GCMGrr77K+PHja/VzK1wlKjU0HQAHfgeflhVOdb91CNF7D7Ats5CRPRNJjPTE3tuOoJ9mo3ZzI2KUG4nHCjhiHYfrp29hZ8gGQGi1NIncgMaz+g1u30ZN6N/o8fL3ln//j7/mHybyz7WY7HzoMno8wqtZhRmBtFhInTGDnIWL8BjeGx/7+ezMs5KXl8fIkSNRpa+zFTQVg+6cxIXn5MkInY68rW/QoZeVFWf+4YtBd5efPxXvydw5P/Hvpm2UWATTBzevM2PgGOFL7toECrYn4z6qaZ20cS0RpbmXx+47wbrMPIb5uNVpe9WlqkwDugkh+gJlAWWrpJTr67RXNxF33nkn//vf/xg6dCgHDhzg/vvvLzcE77zzDv369WP27Nnk5OTQuXNnbrvtNt577z06depEz549mTZtGn/99ddF3hUTJkxg+vTpjBo1ipKSEqxWK0uWLGHfvn3s37+fjIwMOnXqRK9evfD3vzx3NoU6JmygzRD4VjQEDk7O3NKjN5FbtpM5ZQ1NRm5DrH8dkboV3IagUqvo/2AbFrwdxYlxHzHozgBEVhqJk+4lZ+EivKZOuUSDl0Z9NoYhbS1oVP3Y+sevGA0ltM/0IGv1Hoy/9gNAGk1YMjLwfGQq3n38MC5dxOZ9xwkNDaVRo0aQc37e4orGyP3OO3EZ9TlFfTLQ5u6gyFSEg9ZmLEJDgmnSpCnyVDwJi7/nvfX2TH7kfvwb2x7UqSeOEbV8Iaknjp2rz68BQ6Y9j4OrW7WfLT8rgxWfvEtBtm35JNyuB4HRVlwHh9aq/tD1ooe7M+siwq5Jspyaqo9uADbUcV+umJqM3OuKNm3aEB8fz/z587n99tsrnFu7di1//vknH330EQAlJSUkJibSokULfvjhB3r16sWnn35K48YV+5+fn8+ZM2cYNco26bKzs23VbNmyhbvuugu1Wo2vry+9e/dm165d5fkPFG4Qmt0OvV+EFsMuOnVLzz5E7dnP+ug4Jt79GByYC+vfhrDBoFLh6m1Pv0ktWPvjIdYsTmfYE+1x6NqV7AUL8HzwAUQlG6+W/HyKoqJw6NQJtb0Wjvx1Lt3kmWhUIT0ZOHIa8mw6u5YvIj09h/AcidOtbcuT0ztEdMRtzBjY9SM7aUdhcQn9+tkMBaUP9sqE54RKhVvj7mSd/IuuDY1sT97KrcH9y8/fems/jn//PQ3tihBJ8fz28tMEt+0AUpJwYC96R0cate+ESq1BSitHd2zl9xnTGffa2zh72PZXivJyST4SS6OOncrjJqTVyt/ffk56YjzNuvYABKePHCXIEkZ+VDKuvaqPbL4ZuFYZ025+s3kDMHz4cJ577jkiIyPJzMwsPy6lZPHixTRr1uyiaw4ePIinpyfJyRdvcF0q2ru+C1/VG7R20PflSk/p9Xp69OjB2rVrOZV4mtB+r8Ci+yFmMbQZB0CTjj4IFayddYhln+6l75i7KHr+SQoiI3G+7Zy8tjkri6yffyZ73m9Y8/NR2WlxDyvBo1E6GjubK6W0Qt5pVzJGjCTkxAmMzUI44e2GU+MS+j86GlWjikF1xYV5bCWCsCaNCQoq9ZDRnj8juBiHrl1xXLIKj4clMQnzKxgCVUkR2vxszN4NcBrwIFtX/40lNgYnOw09755M2/63o3c497AL7zeApe+/yYI3XmTwY89ydMcWDvz7N2ajgRY9+jDo0adRqdXs/XsVCQf2ctuDj9K2v20AlnBwHxk/xmHeYMGlZ1C9VwytTep2B+I/wv3338/rr79OeHh4heMDBw7kyy+/LH+A7927F4CEhAQ+/vhj9u7dy+rVq9m5c2eF61xcXAgMDGTZsmUAGAwGioqK6NWrFwsWLMBisZCens6mTZvo3Llz3X9AhVqlU6dOODs7s379emSLkeAbDhveAYupvEzj9j7c/kgbslOLWL/PFeEfSPZvv5Wfz/vrL47fehuZ38/EsU0TAvsZcPTOI/OAmuMrAzm6tpXttaopyd+vRqjVBHz8EcMXr6DrkNuJyfNj0bezSTp8TgvLarXyd2weBuzod9u5h/k5Q1C5FLXjLbdgd0BgNmixL9qJ2VJCcvIidu4cxqY/n8S1pAApBO5Zh+g3ZADfNriHf9pNxdCyNzr7is6HgS1aM+7VdzAUFvL7Gy+w9++VNLulB51GjCV2SyQrP3+fs/En2TzvJ0LbR9DmtsHl1waHtyPbJQNNsZrCw7WvSlqfuS4zAiGEGzAL276DBO6XUtbMZ+4GJDAwkCeffPKi46+99hpPPfUUbdq0QUpJSEgIK1as4IEHHuCjjz6iQYMG/Pjjj0yePJldu3ZVuPaXX35hypQpvP7662i1WhYuXMioUaPYvn07bdu2RQjBBx98gJ/f5WV+Urj+aLVaevfuzcqVKzl24gRh/V6F+eNh5/dwy2NQOpINbu3J4CnhrPxqP4ndpxK06FUMJ09RvHcvKa++in2HDvjf3x/9tufBszHOr3yFweBBzuJFWAtKnSdUAqeevXDq26d8hNz9nqk47fuWbWfVLHhzOgHNW9Jt3ET2HD/BvjRJT/W+in9X5UtDlc8IdEFB6PwDcYwroXHbFDZv64XVlIm06nBraaRN9/vIkw1Zv349xQkJPBrkz7psCxN/jKJtoCsvDGpO9ybnlD39moQx/s33ObJ9M6379MfVxxZv4ejqTuTPP3BydxRaewcGTn3yolF/2J23UvxTIikrD9C0VX8UakaNROdqvVEh5gKbpZSzhBA6wEFKmXOp8oroXP3nv/Z9WiwWvvzyS5ydnXng/vth7jCI32xzP+35rC0eofQht/mPoxxYn0S7mG/w97JgiIvDsXt3Ah8bgGrlI+AXDvcsAQePmndg7jBMiXuIafQ8UVv3k2HngsnFnT6avfT2zEQ8suVc2ZT98H0vGD8PWlQeI5P8yitkb/ubUy/nUFLiRMneYDJOGAi/Jxknl0AiIpZiNsOePXvYunUrZrOZgG4j+GFHKql5Jax8ogdhvhdnXruQA/+uIXLuLAY/8SxNO91SaZldM37FrzgIj2nhOAZcxj2ph9RUdO6aLw0JIVyAXsCPAFJKY1VGQEGhPqJWq+ncuTOnT58mNS0NJiyCIZ9AQRrMvxPeDYT3GsJ7DbkldTLubkbiwu+j4HgiTr1uIXC4E6oVUyAgAiYtr7ERsFqtHDp0iJlFg/jYOpm/j6WR2SAUk4s7+rTTeBelIcZfENRYxWZxeZGOHYk3azk+rzUJvwaQnefB8Cffom37LygoPMLJkx+j1+u55ZZbmDx5si0Na0I0i6begrNew1O/78NYjUREer6Bv4whLAt/lN/TXEnLu1hSAyB4bBckcPDbPzlz5IZJrX5Dcz32CBoB6cBPQoi9QohZQoiLIkCEEA8LIaKFENHp6ekX16KgcJPTrl07NBqNLbmR1g46PQBP7IFRM6H9PdD2Lmh7Fxo7Hf3VL2GQOk71ehBfv9Wo9v8KHe6FiUvArnqVSovFwt69e/n6669ZuHAhBrOVNu070tbhLO3Nexmt+ocmlkTWnPBi/+64ihdrSgUGzBcbguLcbLZ9/RoL/vyduAae+Ni7cfp2b35pdwhVY2+8vPoQEDCBxNOzycraBtjStA4cOJATJ04QH3eA98a04XBKHp9dQmvpTE4xbyyPocf765m56QQeTnbM2RZPz/c38PLSgxQaKubI8mndFKOvBT9TQ5bOeIM/3nyJlONHKq1bwcY1XxoSQkQAO4DuUsqdQojPgTwp5WuXukZZGqr//Fe/z+XLlxMTE8MzzzyDvf0lVFukhJMb2PP7Rraf7o2dzkjbvg0IH9gCvcPFqqPnYzKZ2Lt3L1u3biU3Nxc/Pz969ux5Thm0OAfm3wXGfMx3/M6KH37k5J5d9J74ABFDS2NGCzPhw0Yw+APoYotlKMjOYveqZexfvQyT2UpjLzOBiSp8vP3Rf/l/jP5zNMEuwcwdPBeVNBG1ayQGQxpt287C3a0TUkrmzZtHfHw8U6ZM4eNNKfwRfZoFU26hU4htdnMyvYBvI0+wdK9N6Hh0hwAe6dOEUC9HEjOL+G7TCX7bmcjTt4Xx5G0Vg8hMGcWkfRxNkX8x6w//iqGggBHPvUJIu6p1nOobtZqYpjYRQvgBO6SUIaXvewLTpZRDLnWNYgjqP//V7zM5OZmZM2cyaNAgunbtWm35lOM57F6TQEJMJjo7Na37BNK2XxAOLhcrWZ49e5Zff/2VvLw8goKC6NWrF02aNLnYrVJK20ulwmI28dcXH3F051a633EPXUaPJyv+GDv/bwLHixsghc2/xGIyAZJmLmfp0tINr/y9pMY0ICcOwrZGsm77WzyXvJbxwp1Xen+AwS+EvXsnUVJyhjZtvsfTowf5+fl888036HQ6uvboyfT1uSTlGtCqbf0rMVnRa1Tc1bkhD/VqRIDbxYZy/PfbySgw8M8zvS/6XNmLj1G4Jw3Xqc1Y+tVbZCUlMuSpFy/aW0g9fpSdy/4gMymR8H4Dadt/MDp7B/LSzxL152JO7N5J/wcfo1GHmy+/+A1rCACEEJuBB6WUR4QQMwBHKeXzlyqvGIL6z3/5+5w1axbFxcU89thjNdbvT0/MZ/eaBE7sPYtaI3FrZaDn4AgCgn0ASElJ4ZdffkGlUjFmzBhCQkJq7FdvtVj4+9vPOLx5A36Nm5J64hgaYaFFM3/0TXtgLDZTlG2ik3kxDexS4ZGtkLyP/A8nkRTpROCtJpy90/m4YXPmqIsYk1/Aa04tsfR7nr2pH1JYeJIWzf8PP78RJCaeZvXq1aSmpuLo5IzBuwUqL1tfXR20jOsYhLfzuYQzZrOZQ4cO0bx5c/R6PfN2JvDK0hhWTetBqwYVl8jMOQZSP9yFQ3sfHAY1YMn/vQEpJpp26o7VTyCRJMXGlAe2eQYGk3zkMHaOTgS0aMWpvdGAwMnDg4KsLIZMe46wrj1q9qXeINzohqAdNvdRHXASuE9KmX2p8oohqP/8l7/P/fv3s3TpUiZOnHhRlHl1ZKcWsvC3ZSTnHQMp8HMJoeMtbfhn4xrs7OyYNGkSnjXQKLoQabWyfs73xG3dRLuBQ2h/eDqG8EfZUzCSozvTsFolaoy0aKej/dhbcPGyx3J4PScmPIqlWOB0S3s8n3qRH03/MvPQT9xeYuGdjGzkHbPYlzubvLx9ODg0ISR4Kj4+Qzlx4hSbNm0iKSmJHj16cOutt1ZquNasWcOOHTsICAjgnnvuocSqptM7//BAz1BeGnzx30/OihMUbE/GdUgjCqNSMKfZ9jnyTVnE5uzkrDqJDkOG027A7ejsHUg5doSdyxZyJjaGlr36ETFsNDp7e5a89yYpR+MY9OhTtOzV77Lv5/XihjYEl8uNbAjeeecdfvvtN9RqNSqViu+//54uXbpUec3rr79Or169uO222/jss894+OGHcXC4+lDyGTNmlIvWXS2TJ09m6NChjB079qrrqgk3yvd5PTCbzXzyyScEBQVx1113Xda1J06c4JdffqFl89bkpho4k30chMRO48T4sXcT2rzBVfVNSokQgr0vT2Vb1jg0WjUt22tocvwZ4pwfJy4pCCkhrJMvHQYF42pnJOvXeWT9+ivW3Fwcu93Czv6B/M+whNZWNVOyMuk5dCbpbmbiE76lsPAodnZBBAc/jK/PKNas+Yfdu3fTpUsXBg4cWGGGdPLkSX7++WdCQ0NJTEzE29ubiRMn8vgfhziSms+WF/uhukC731JgJPWDXUijFY23Pc59ApEaKNyYjCm5EI2XPV4PtkbjVnVSelNJCcs+fIvEQwdo2aMPnUfegWdg3ecmuFpqO2exQiVs376dlStXsmfPHvR6PRkZGRiNxmqv+9///lf++2effcY999xz1YbAbDZXX0jhhkSj0dChQwe2bt1KTk4ObqXy09VRXFzMsmXL8PLyYuTo4eh0OtKSM1m/cjs5h3X89VkcIW0y6Dg4GL/QK89/u3PFSaKz7qCx32l6PXMXDvs+h8Qj+D85lE4GB/atO82hzWc4EpVKo7beeITcjnyhP4ZDB3GJnEfrN7fzc4tgfulYwBOBFppufoa+AT1R+Q3GyakFgaYDHDnyGqdOfUm79o+h1XZhx46dlJSUMHDgQBwcHMo/q6enJ3fddReJiYn8/vvv/PTTTwyOGMiGI+nsTswu32g+fPgwaWlpAJjbFhPiFUSTnq2Jis8iNa+EwY+2xXI8l6zf40j/7gBptzdkQ1oeVQ2LZcTdqFTuHNm5jUPbNuMe3pFBd99DQHDoFd/bG4V6YQjK1hhrEz8/PwYPHlxlmZSUFLy8vNDrbWuYXl5eREVF8fjjj7NkyRKWL1/OnXfeSW5uLlarlZYtW3Ly5Mny0XZycjLJycn07dsXLy8vnn76aV5//XXA9k9uNBo5deoUu3fv5plnnqGgoAAvLy/mzJmDv78/ffr0oVu3bmzduvUi4bkffviBmTNnYjQaadKkCb/88gsODg5MnjwZFxcXoqOjSU1N5YMPPmDs2LFIKXniiSdYv349oaGhiq7RNSYiIoKtW7cSHR3NbefpCVXFqlWrKCws5M4770Sns20W+zbw5K6Hh1JSaOJgZBL7159m8fsZBDRzJ2JwMAHN3Gu8VyClZNvi4+z75zQt3KLo0/IIKpd7IXEneDcHBw+cHKDHHU3pODiY/etPE7PxDCf3lbl7e6Bv9zS9x6fivOB7Hvo1k0khfixpm8kPmu3I9B1lLfFy+D2EcYyjR9+gUaP70Ol6sWnTJmJjY4mIiCAnJ4f8/HwefPBBdDodTZo04Z577uHnn38mJOsEdloVf+5LplOIB3l5eSxatAir9VxcwnZVNI2K85ixKRezVfKBmz1TezeiycAg3FfEw29H+JMiEinVaALKrhbI0iNgT2PaNLSjmSaDAoOFn2b/xP2PPIq3t0+Fe6et4/wBtU29MATXiwEDBvC///2PsLAwbrvtNsaPH0/37t3LNYU2b95M69at2bVrF2az+aIlo2nTpvHJJ5+wYcMGvLxsIfZlD/Q77riD3r17YzKZeOKJJ1i+fDne3t4sWLCAV155hdmzZwOQk5PDxo0bAdvSUBmjR4/moYceAuDVV1/lxx9/5IknngBsBmzLli3ExcUxfPhwxo4dy9KlSzly5AgHDx4kLS2Nli1bcv/999fdzVOogJubG2FhYezZs4c+ffpUm97x4MGDxMTE0Ldv30qTIdk5auk0JJS2twZxaHMy+9YlsvyzffiGutBxUDAhbbwuaRCklCQdyWb3X/GcOZpDeJ9AembPQJj9wWqF01HQumI6EntnHV1HNKbriHN7HHkZxSz/bC8bYn0Z+s0CHGK3kDlzJnctN3HvLi88WxtwcD3Cy0GB/N/BJTza6kH6BTTjdNJPBDQoYerUx9iyZSvbt29HSkmfPn0qfNaQkBCaN29OzIF93NqsH38dTOG5Ac2YueQfLFYrsR7defDWcHqEuvLVDz9xdPs6enu1Zmz/bvzxTxQ7Vi9kvzCRr2/AU9ZQfjE4ldcttCpKwu3Yaz3J4aOxFYwKCBKlF0V5RbRwyOXrr79jlbkVOfLcrP6uzg15d3RF7bEbmXphCKobudcVTk5O7N69m82bN7NhwwbGjx/Pe++9R5MmTYiNjSUqKopnnnmGTZs2YbFY6NmzZ/WVAh988AH29vY89thjxMTEEBMTQ//+Nt0Ui8VSIf/ApRLTxMTE8Oqrr5KTk0NBQQEDBw4sPzdy5EhUKhUtW7Ysnz5v2rSpXOK6QYMG5ySIFa4ZnTt35siRIxw6dIi2bdsCkJqaSkpKCu3atSt/cOfm5rJq1SoCAwPp0aNqLxadnYb2/RsS3ieAuO2p7Pk7gb++PYhngCMNW3lSmSfpmaM5nI3Pw8FVR8/xYYT3CUDMtrdpDaXHgiEXgqp3dXXxsmfUsx1Y/tk+Vnwdw+2P9qLRymHkr/uHjJnfk7I2Fo13K6a3BccmKXxzaBbFrq0Y3nwyCclzMFsKGD78Vfr27UtCQkL5Pbnwnh0+fJiubkWsKjTS9d21DFPFIu28SClR8/Avuwn1cuR0RiCjXcwE5cVwbEMywblZ2Lu4oLZzxSkjnuV2qTQLCEWntj0SM7OzOHn4DBqpprVXYzxbByDUKlQqFS1atEDr6MKyPUmcXfkjCCOjHI5g598UKa0Upp7hzL87+DEhkqa+zgS1DCf0Bo9fqBeG4HqiVqvp06cPffr0ITw8nLlz59KzZ09Wr16NVqvltttuY/LkyVgslvK8BFXx77//snDhQjZt2gTYRmetWrVi+yXy2Do6Vp6Wb/LkySxbtoy2bdsyZ84cIiMjy8+VLWWV1V+GItt7fQkNDcXT05Ndu3bRtm1b4uPj+e233zAajaSkpDBo0CAAli1bhsViYdSoUajV6hrVrdGqad0rgBbd/Tm+K409axM5sD6p0rJOHnp6392M5rf4odGW1q+1B2MBJJYu5zSs3hAAOLnbMerZDvz5+T5WfX2AQQ+3JmTQQJwHDqBw82Yyvvue9H/2MCHam1btLHzWNgZj/B7GtWhLfNpK0tPX0aDBeFq2fLDSz9qwYUO8vb0pTIqjdYOWNNLk4HDWxN1jBvBu4yasPJDC95tOcGsrf14bM4BVfy4jOzub0aNH06pVK9RqNUlJSWzevJlDJ46V/z/o9Xp6dO5Gy6IArLuzcAzyx31kkwptT+oWSnGbF5n94pPkePhjOBOH1WpBjSDY1UzGzn/JFZJdfy6m/0OP0ebWQeWb7zcaiiG4Co4cOYJKpaJpU1tU4759+wgODqZXr15MmjSJSZMm4e3tTWZmJqmpqbRq1eqiOpydncnPz8fLy4uEhAQeffRR1qxZUx5l2qxZM9LT09m+fTu33HILJpOJo0ePVlrX+eTn5+Pv74/JZGLevHnV5lLu1asX33//PZMmTeLs2bNs2LCBu+++u8prFGoXlUpFp06dWLNmDVu2bCEyMhI3NzdCQkKIiorCZDLh7e3NqVOnGDZs2BW5harVKpp19adZ18vMaqd1gMIMmyFw8gX3kBpf6uCiY+Qz7VnxxT5Wf3eQ/g+0oklHH5x69cKpVy+Kdu0i4/uZhEdu4cdIADVGYvHxV2N80I8z1l85c+Y3fBz64vgXGJbvQHd7J4qH6zhbsoH27aeydm063z4whH/++YdcNzeaNGmCSqVieFtvAq3PoNW44Gj3DXfeeedF/QsMDKzSWyvH/iQFm89g18ID+2YVNZ3snZwZMuUJFr9jE0YIaduBBl178vemLWwJG4lrQGNGZv7Duplf8cXfh0kL6syyx7qjVt1YxkAxBFdBQUEBTzzxBDk5OWg0Gpo0acLMmTNxdHQkLS2NXr16AbYsZj4+PpWOBB5++GEGDx5cvvmbmZlZnpmsQYMG/PXXXyxatIhp06aRm5uL2WzmqaeeqtYQvPXWW3Tp0oXg4GDCw8PJz8+vsvyoUaNYv3494eHhhIWF0bt37yrLK9QNbdu25d9//+Wff/7B19eXiRMn4ujoiIODQ/ksMSwsjA4dOlzbjmntbEtDp3fYZgOXOaq1c9Qy4qn2rPx6P2tnxXA6tgFanW2E7x0cRLNZP1B8MIbCXVHsOLOdbSnb6Jgiaf52MlpPDZmjNaS1Wgs9QTa3R3j9C7mASY3ZZT56/WDWrFnDmTNn6N+/f7nb6clTX5Cba3M9P3rsfzQL+99lj8hdB4RQcjSb7EVH0T3VEbVjRVmPkDbtGfXiGzi6uePbqAlSSmLjE1GnpTHnlAexdl3o4pBF0/RdOJWkMevDnYS5mHHMP0Z4mDv2laXV7DYNXK5dCloljkDhhkD5Ps+xceNGTp8+zejRoyu4FW/dupWYmBgmTJiAk5NTFTXUAcsfg4OLwFwCA9+FWx69ompMBgvrZh/izBFb/KhVgtlgIeL2EDoPCy1/SM89NJdZe7/GNaOAwVEqWsZLdkeoKeljpamTkROFWjSbNPQ5bSD/URMm0x3s2K5Ho9HwzDPP4ODgQE5ONLv33EUD/7Fote4kJH5PWNgbBAVOuux+G5MLOPv1Puybe+A22rYCIAQIe02lhiUhIYGffvqJEp9WHBcBjA61cmzHPzbfI4sZ+5yzqLLS0WKmlUcWnbxScdOd53r+wFrwufr/ByWOQEHhJuVSs7Hu3bvTvXv3a9ybUrQONiMANd4fqLQavZrbH2lT/t5qlUT+Gkf0X/GYjBa6j7FpId3b6l7ubTkJFt0HzVfAR//St0G78uuyUwvZk5/IruLTNE94EV3wGlSqIbRu3Q4HBwfM5nwOHX4Oe7tAmjZ9BbXagcKiExw9+hYO9qF4etbMcaMMXQMnXAcEk7s6nuJD59LR6oJdcO4bhF0zd2SxmYLtKRRsPYPOXkOodxCp+Sd5oW8wq1evJjAoCM+wDqxYG0mQpwY8bcF+W4GtZguNMzIY9OAUvNu2q7wTdYhiCBQUFKqnLF2l1sGWCKeWUKkEfe9pjkavZv8/p8lJLcLZ47woX/PTUBgCX/wJrnsAKCyx41SaL2qVFXudieS4u2kQ/B1DhjrSvFl/jMYsjhx5nZKSM3TsMB+NxjZ7atXyE3bvGU/MoSeI6LgIR8cmlfTo0jj1DETtZoe1wDZyt5ZYKNyVSuacQ2h8HbDkGJAGC3bNPbDkG2mb5Mcp/Wn++usvQoJDuHvC3Wi1Wg7s3sqEuAXElXTEnJmHwdmZY8FBnHD3ZvbnH9AEQd9np+PR6tq5nyqGQEFBoXrKktMEdAR11dLXl4tQCXre0RS9g4ZDm5M5m5BXsYClFxjyIc+2jK0WZjq4raatyz9k5rvwZ/YM/E94ktv4N84kWzlzZj5WazGNGz2Lm9u5VRGNxpE2Lb9h196x7D/wEJ06LEKrdgKNnpogVAKHtt4Vjjn3DqRoXzoFO5Kxa+6Bc58gtJ5apFqN64kQ2izPojCvkN6nm1CyKZnC7P2MXzQHYwZ4eiSxpt0gfnVqgUrCvfbHKAlqyqnTx+l7dbf0slEMgYKCQvWUzQgaVp4e8moRQtBlWCO6DGtUwytsGlj2v47DN/ooSUcmEdT4U5KS5uLrM4zgkKk4OVbMUWDOzib1wWdwV1lIfziJ/Wu60uGUDtXDmy8vzef5/daocIzwxTHCllfZcOoUxwc9gLZBA4K+/ZbRz0/EEJ9L3rpT5P1zGmsRaO2cKeyt4263Z2no5cQ7fRoTeSSdebGCRwJTyVKpKHB25Vom2VQMgYKCQvWUzQgaVi2oeK0RbcbR/fBHLMl6D9NvXUhpUcSmcFfsNm/E6rCOkpA0UEtaE0CHd5ZhPn0aVx8Lxp8FOfdZ2Kctot2fT6Ia//NleUIZExLIWbYM+/BwnPr0QahUlBw9SuL9D4DJRPH+AyROvo+gWT+gdoWCNW9jPGvEqdfTaCKexrNXJj826kLPpl5o1CoGtPRjYEI2K4ua8HLfFgQGBtbhXbsYxRAoKChUT2AEhPaqsxnBFdPsdvwdphGkTuFU6t34R+6i+EhnpMqmvaTf5U+K+2Y8/vma/HwTxwYV06VlU7y8h2E9vJzslvFEHdxCgx1f49phIq76i8X5TGlnkSbbvoAlM5OsuT+Tt2aNTW4D0DdtiuuY0WR++x1Cr6fhL3MwnUkl6cknSZx0LwiBMT6ewC+/QF8cR3qkL4VRYXRvY4+mVJPI3VHHh+Pacu/sKDbl+dG7hnkpaoubSxnpBiI+Pp7WrVtXODZjxoxqo4ejo6OZNm0aAJGRkWzbtu2y2w4JCSEjI6PK47t37yY0NJS9e/fy559/8t577112O5URGRnJ0KFDa6UuhZuIBu3h3hWgqzyS/bqhd4Jmg+im+x6r2oGUwF4ElByj684ZtD3wNb4ZGYSeHUpq8GMsH+/EW62dGapKZWjmD9zvlMrpg2oKw1Xs3f85/X/ryatbXuVU7imklBRu20bCpHs53rs3J27rz4nb+hM//k4KIiPxvP8+mkRuoMEH7yOllbPvvY/KyYng2d+iX3MXTinfEvT9dxjPnMF4+jRB33+HU+/eaFOW49PgR4S9lvQfDmKIzy3/KL3DvJl0SzA/bjnF7oRLpmepE5QZwTUmIiKCiAjbBlZkZCROTk5069atVts4cOAAY8eOZcGCBbRv35727dtfpE6qoFBvaD0Wr0MTGH2nFac2PXBy7U3Rd7GYolfS2rqN4w6T2MlAhkUs5M7OJRzJqpjIXrXxa5zCTvBeUhH5MX+w/+AC4gt1eK4xY2/0w/vpp9H42NRFhUaDU88eoLdwctt9GBt4It9riiXDA6cGfdAc/AyyTkLWSRwb30rookWARN+oEWeTFpEv9hHY6l68I9qS8cNBMn6MwfPeltg1cQfgpcEtaOTlSNvAK5cNvxLqhSE4evQt8gtia7VOZ6cWhIW9dsXX9+nThy5durBhwwZycnL48ccf6dmzJ5GRkXz00Ud89dVXfPfdd6jVan799Ve+/PJLmjdvztSpU0lMTARsuQq6d+9OZmYmd911F+np6XTu3LlKiejY2FjuvfdefvnlFzp37gzAnDlziI6O5quvvrqkDLXVauXxxx9n48aNhIaGYrVauf/++xk7dixr1qzhqaeewsvLq0JEa1ZWFvfffz8nT57EwcGBmTNn0qZNG2bMmMGpU6dISUnh6NGjfPLJJ+zYsYPVq1cTEBDAihUr0Gpr1/NE4T9M0/6gd8U/exG49oFlU3FMXwhDx0GvF+jo1ZSMHw4RteIUY1tGMKJJ84rXNxnB0TljSLHfh5u9FasQWEJLSO8AqdYSwpq40ClkZHlxc0Ey+zcMIEdfhP1ZwMkXq0aQkbSVBDsLwb2G0CClBPW619BP6QPeYZw5M5+4o69BQ3sSxVL8U3X43TOS3CWZnPljHW5DG+MR3g57nZrJ3a99fgNlaagOMZvNREVF8dlnn/Hmm29WOBcSEsLUqVN5+umn2bdvHz179uTJJ5/k6aefZteuXSxevJgHH3wQgDfffJMePXqwd+9ehg8fXm4oKmPEiBF89dVXVapSlslQr1y5kunTpwOwZMkS4uPjOXjwILNmzSoXuSspKeGhhx5ixYoVbN68uULehzfeeIP27dtz4MAB/u///o9Jk85FbJ44cYJVq1axfPly7rnnHvr27cvBgwext7dn1apVl38zFRQuhUYPLYdB7EpYeC8cXAi3vg5jZoF3GEII+tzdDDsnLet+OozZZLmoirDJi+nddAa9D2XQN78pHbr8RaZDT1zIIu/kK2zYPpTMrC2Y8k6xd9Ot5OqKaG03gm4nPOi26RTdnZ6g3REL9lYHjrKTrUEJxDd0wLzsARJPfEXckVfxzJF0OazCz28UyckL2H14LMebP01815fYl3EHZ+dFY0yqWgqmrqgXM4KrGblfKZfSKzn/+OjRowHo2LEj8fHx1db5zz//cPjw4fL3eXl55Ofns2nTJpYsWQLAkCFDcHd3v2Qdt912G7NmzWLgwIGXVKasTIZ6y5YtjBs3DpVKhZ+fH3372jyZ4+LiCA0NLRfWu+eee5g5c2b5NYsXLwagX79+ZGZmkptrW/McPHgwWq2W8PBwLBZLuXJmeHh4je6FgsJl0Xos7P0V4lbCoPeg6yMVTts5abl1UgtWfLmfZZ/sxcndFjvg5G5Hm76BuHjZQ4eJ/H979x5dVX0lcPy78+Im5sEjIQYCJFKihiTEGFBsIIrFF+BgxVYFq9DRMuCLx9i6rMu01FkWO1Ux0wHXqm8GXBOqIaGD40TU1sLwjDFBomYASRMJBgheSCSP3/xxTsIlhLy5N/fe/VnrrnvzO79zcvZNcnbO75y7fy2BoVSse4Xq379N9sx/Iizrd2wp+zVD6ndQXHwvgc1CS0gLqdE/Iyb95zChBl6fjbyzkGFBoQy74z2OBR3n4IE/UBH/EQea/07zweeIOfIdKaczCfhhDpePSCcx8WHq6nZhMJjGFhr21nK6ooGa0mIGJQ1h8K1jCY4Oddvb5xOJwBOGDRvGsWNnX9A5evQoiYlnTutayz0HBgZ2ayrJlpYWtm7d2lZ51FV3C2Xl5uaycOFCFi1axJo1azrs01EZ6s6GmzqbwOR8fVu/R0BAAMHBwW3tAQEBOq2m6n+JUyH1Dhg7DdI7rpo7evwwJt82ln3bvuZo9Skwhv0l31CypZKkibHEJkbyyfsjqTu+DKGFT185SXxUIZlTvk9czT6qT1fwdVwECWOXMizlQWuj4cPhvkIoeASS/wGixzEEGJL+Cie+LeXg9kcIaTjNuKznCBh55sNtDkccDofLTRfx0DK1Cee2ak5uqyZgUPfKi/cXjw0NiUigiOwRkUJP7UNfhIeHExcXR1FREWAlgc2bN3c5UYir1hLUrW644QZyc3Pbvi4uLgasEtFr164FrGk52ycgVwEBAaxbt47y8vK2aS+7Iysriw0bNtDS0sLhw4fb5i+47LLL2L9/PxUVFQCsW7eubR3X/frggw+Ijo4mMjKy299TqX4TEGgNBZ0nCbTKuHEMdz91lfXIuZp7VlxD2nXxVOyp4aP1nxM8KJCbHkjhp89cyeSMr6l1RvFOYQwVh2IZecUKrrz50zNJoFXYUPjxG5A656zmyIgUUq8v4tJb/nJWEjhvCI4gIq8dxcWPTSQwIqTHb0FfePKM4BHgM8Brjxyvv/46ixcvZtmyZYA1Zj527Ngu1jpj1qxZzJkzh/z8fF588UVWrVrF4sWLSUtLo6mpialTp7J69Wqeeuop7rrrLjIyMsjOzmb06NGdbnfQoEHk5+eTnZ1NbGzseSevcXX77bdTVFRESkoKSUlJXHXVVURFReFwOHjppZeYMWMG0dHRZGVlUVpaCli3y86fP5+0tDTCwsJ47bXXuh27UgNB+JBBZN1hzbn8bW0DMaMj2s5eMx64m7STJ3nnX3fw4bF/JC7pasJDuleOoi/EA3MVeKQMtYjEA68BTwNLjTGd3piuZajdw+l0Eh4eTm1tLZMmTeLjjz/m4osvdsv31p+nGqiOHz7FW09v5+JLorj14XSPHKh7q7tlqD01NPQ88BjQ0kU/5UYzZ84kPT2dKVOm8OSTT7otCSg1kA2ODSPrjnFU7jtGyQcdT+/p7dw+NCQiM4EaY8wuEbm2k34PAA8AXQ6FqP7hOq+xUuqM5KwRHCj5hq1vVxA3NorhY7x2RLtDnjgj+D5wq4gcANYD00TkzfadjDEvGWMyjTGZMTEx7RcrpZTbiAjX3XM5YREh5D9fzNf/V9f1Sl7E7YnAGPO4MSbeGJMA3Am8b4yZ5+79UEqpngiLDOG25RmERgST/0IxleXurQd0Iekni5VSqpsihjq4bVkGkcMcFOZ+QtUXvpEMPJoIjDEfdHXHkFJKDSQXRQ1i9tIriBjq4L2X9/LdqUZP71Kf6RlBLw3EMtQvv/wyqamppKWlkZKSQn5+PmAVnauqqupyu93tp5S/Cw0PYfqCZE7VnebDdZ97enf6TEtMuNmFKkNdWVnJ008/ze7du4mKisLpdHLkyBHAOsCnpKQwYsSITrfR3X5KKRg+JpLMGQlsL9hPYlo04ybGUnPwBJ8UHWLEuMGMnzLS07vYbT6RCJ78opJSZ32/bjMlPJQV43o/XZy7y1DX1NQQERFBeHg4YJXACA8PJy8vj507dzJ37lxCQ0PZunUrzz77LAUFBdTX13PNNdewZs0aNmzYcE6/vXv3snTpUpxOJ9HR0bz66qvExcWxatUqVq9eTVBQEMnJyaxfv77X75NS3uzKm8ZwsLSWD9eVs29bNV+VHQWBL3fWEDM6olu3mTY1NnP8cD3R8eFu2OOO6dDQBeTOMtQTJkwgNjaWxMRE5s+fT0FBAQBz5swhMzOTtWvXUlxcTGhoKA8++CA7duygtLSU+vp6CgsLz+kXFBTEQw89RF5eHrt27WLBggU88cQTADzzzDPs2bOHkpISVq9efYHfRaUGroDAAH5wXzItzYYjX33L1bMv4Z7fTCY0MoT/eWUvjafPLXnt6nR9ExtfKOat32xnz3+fv7z8heYTZwR9+c+9twZaGerAwEA2b97Mjh07KCoqYsmSJezatYucnJxz+m7ZsoWVK1dy6tQpjh49yvjx45k1a9ZZfcrLyyktLWX69OkANDc3ExcXB0BaWhpz585l9uzZzJ49u8u4lPJlg2PDmLdiMiGOQIJCrKqh1997ORtfKGbr2xVM/XFSh+s1nGykYFUx3xxyEve9KP72py9pPN3MxBkJ3a423F98IhF4wkAsQy0iTJo0iUmTJjF9+nTmz59/TiJoaGhg0aJF7Ny5k1GjRpGTk0NDQ8M52zLGMH78+LYJalxt2rSJjz76iI0bN7JixQrKysoICtJfJeW/wiLPrhY66vKhpE2Lp+T9SkYnDyUhNfqs5c5jDRTmlnDs8EluWpjKmJRhbHnjM3YU7qfpu2Ym/3CsW5OBDg310kArQ11VVcXu3bvPWnfMmDHnfJ/Wg350dDROp5O8vLwO9+fSSy/lyJEjbYmgsbGRsrIyWlpaOHToENdddx0rV67k+PHjOJ3ObseslL+YPHssQ0dcxKZ/K+HP/17C4QMnOPFNPR/+RzlvPrmNuppTzFw0gcS0aAIChGn3XE5K9kj2vPcV1V+695PL+m9cHwykMtSNjY0sX76cqqoqHA4HMTExbeP39913HwsXLmy7CHz//feTmppKQkICEydObNtG+355eXk8/PDD1NXV0dTUxKOPPkpSUhLz5s2jrq4OYwxLlixh8ODBfXsjlfJBQSGB3LYsg5L3D1GypZL9n+xExCozfdnkODJuHE1UTFhbfwkQpt6ZxLjM4YwYN9it++qRMtQ9pWWofZ/+PJUvO93QxN6/VtHgbCQleyThQxxu+b7dLUOtZwRKKXWBhTiCSP/BwK2irNcIlFLKz3l1IvCGYS3VNf05KuVZXpsIHA4HtbW1ehDxcsYYamtrcTjcM2aqlDqX114jiI+Pp7Kysq2ejvJeDoeD+Hj3fyhQKWXx2kQQHBx81oe3lFJK9Y7XDg0ppZTqH5oIlFLKz2kiUEopP+cVnywWkSPAwV6uHg2cO52X79O4/Y+/xq5xn98YY0xMVxvyikTQFyKyszsfsfY1Grf/8dfYNe6+06EhpZTyc5oIlFLKz/lDInjJ0zvgIRq3//HX2DXuPvL5awRKKaU65w9nBEoppTqhiUAppfycVyYCEXlZRGpEpNSlbYKIbBWRT0WkQEQi7fbpIrLLbt8lItNc1rnSbv9SRFaJO2eL7oWexO2yfLSIOEVkuUubT8ctImn2sjJ7ucNu99m4RSRYRF6z2z8Tkcdd1vG2uEeJyBY7jjIRecRuHyoi74nIF/bzEJd1HrfjKxeRG13avSb2nsbdr8c2Y4zXPYCpQAZQ6tK2A8i2Xy8AVtivrwBG2K9TgL+7rLMdmAwI8F/AzZ6Orb/idlm+AfhPYLk/xI1VSLEEmGB/PQwI9IO47wbW26/DgANAgpfGHQdk2K8jgM+BZGAl8Au7/RfAb+3XycAnwCAgEajwxp95L+Lut2Obx4Pvw5uW0O4P5ARnLn6PAvZ2sI4AtfYvTBywz2XZXcAaT8fVn3EDs4FngRzsRODrcQO3AG92sL6vx30XUICVCIfZB5Gh3hp3u/cgH5gOlANxLj/Pcvv148DjLv3ftQ+CXh17V3G369unY5tXDg2dRylwq/36Dqw/kvZuB/YYY74DRgKVLssq7TZv02HcInIR8HPgV+36+3TcQBJgRORdEdktIo/Z7b4edx5wEqgGvgJ+Z4w5ipfHLSIJWP/5/i8Qa4ypBrCfh9vdRgKHXFZrjdFrY+9m3K76dGzzpUSwAFgsIruwTqtOuy4UkfHAb4GftTZ1sA1vvJf2fHH/CnjOGONs19/X4w4CsoC59vNtInI9vh/3JKAZGIE1PLJMRC7Bi+MWkXCsoc1HjTEnOuvaQZvppH1A60Hcrf37fGzz2olp2jPG7ANuABCRJGBG6zIRiQfeBn5ijKmwmysB12mx4oEq9+xt/+kk7quAOSKyEhgMtIhIA9YvmC/HXQl8aIz5xl72Z6xx9jfx7bjvBjYbYxqBGhH5GMgE/oIXxi0iwVi/q2uNMX+ymw+LSJwxplpE4oAau72Ss0cAWmP0ur/xHsbdb8c2nzkjEJHh9nMA8Etgtf31YGAT1hjix6397VOsb0XkavuK+k+wxuS8yvniNsZMMcYkGGMSgOeBfzHG5Pp63Fjjw2kiEiYiQUA21ji6r8f9FTBNLBcBV2ONE3td3PZ+/hH4zBjze5dFG4F77df3ciaOjcCdIjJIRBKBccB2b4u9p3H367HN0xdEenkRZR3WWGgjVvb7KfAI1gWyz4FnOHNB7ZdYY6fFLo/h9rJMrDHXCiC3dZ2B+uhJ3O3Wy+Hsu4Z8Om5gHlBmx7jSH+IGwrHuDisD9gL/7MVxZ2ENZZS4/M3egnURvAj4wn4e6rLOE3Z85bjcIeNNsfc07v48tmmJCaWU8nM+MzSklFKqdzQRKKWUn9NEoJRSfk4TgVJK+TlNBEop5ec0ESjVAft+/L+KyM0ubT8Skc2e3C+lLgS9fVSp8xCRFKx7868AArHu077JnPkEZ0+2FWiMae7fPVSqf2giUKoTdomOk8BF9vMYIBWrPEuOMSbfLhD2ht0H4EFjzN9E5FrgKawPhaUbY5Ldu/dKdY8mAqU6YZdr2I1V3K0QKDPGvGl/vH871tmCAVqMMQ0iMg5YZ4zJtBPBJiDFGLPfE/uvVHf4TNE5pS4EY8xJEXkLcAI/AmbJmdneHMBorIJeuSKSjlUBNMllE9s1CaiBThOBUl1rsR8C3G6MKXddKCI5wGFgAtYNGA0ui0+6aR+V6jW9a0ip7nsXeKh1/lcRucJujwKqjTEtwD1YF5aV8hqaCJTqvhVAMFAi1oTyK+z2PwD3isg2rGEhPQtQXkUvFiullJ/TMwKllPJzmgiUUsrPaSJQSik/p4lAKaX8nCYCpZTyc5oIlFLKz2kiUEopP/f/XEs5agelUigAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = marriage_df['Year']\n",
    "y1 = marriage_df['CrudeMarriageRate']\n",
    "seaborn.lineplot(data=marriage_df, x=\"Year\", y=\"CrudeMarriageRate\", hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb54bec",
   "metadata": {},
   "source": [
    "## Data Cleaning: age_df (Average Marriage Ages)\n",
    "\n",
    "In order to clean the data, we changed the name of Average Marriage Age column from \"Estimated average age at marriage, women\" to \"AverageMarriageAge\" to avoid any SQL errors.  \n",
    "\n",
    "Similarly to the previous dataset, the \"code\" column was removed because of the same reasons which is that it wasn't relevant for data analysis/exploration as it was redundant. \n",
    "\n",
    "Similarly, the dataset had 183 different countries and plotting all the data in one graph would be difficult to analyze. Therefore,at first, we also decided to limit the data to only the United States, United Kingdom and Hong Kong (country in Asia) to get a diverse range of data/countries which we mentioned earlier why we decided not to go for this route. \n",
    "\n",
    "Similarly, as mentioned above, we manually listed out all the countries in the dataset to see how large our dataset is, the population, and the continent to be able to compare all the data. The countries includes UK, Italy, Germany, Austria, Switzerland, Japan, Mexico, Canada, and Australia.\n",
    "\n",
    "## Data Description: age_df (Average Marriage Ages)\n",
    "Similarly, for cleaned age_raw_df, we have the same columns but with observation of Average Marriage Age. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc79648c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>AverageMarriageAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1972</td>\n",
       "      <td>18.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2010</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2015</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2002</td>\n",
       "      <td>22.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2009</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2010</td>\n",
       "      <td>20.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2014</td>\n",
       "      <td>20.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>20.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2017</td>\n",
       "      <td>21.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1935 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Entity Code  Year  AverageMarriageAge\n",
       "0     Afghanistan  AFG  1972           18.100000\n",
       "1     Afghanistan  AFG  2010           21.500000\n",
       "2     Afghanistan  AFG  2015           21.500000\n",
       "3         Albania  ALB  2000           23.900000\n",
       "4         Albania  ALB  2002           22.900000\n",
       "...           ...  ...   ...                 ...\n",
       "1930     Zimbabwe  ZWE  2009           20.900000\n",
       "1931     Zimbabwe  ZWE  2010           20.600000\n",
       "1932     Zimbabwe  ZWE  2014           20.299999\n",
       "1933     Zimbabwe  ZWE  2015           20.200001\n",
       "1934     Zimbabwe  ZWE  2017           21.100000\n",
       "\n",
       "[1935 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_raw_df = pd.read_csv('age-at-marriage-women.csv')\n",
    "# print(age_raw_df.columns)\n",
    "\n",
    "age_raw_df[\"Year\"] = age_raw_df[\"Year\"].astype(int)\n",
    "age_raw_df = age_raw_df.rename(columns={\"Estimated average age at marriage, women\" : \"AverageMarriageAge\"})\n",
    "\n",
    "age_raw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cb9916c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable age_df\n",
      "Returning data to local variable age_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Year</th>\n",
       "      <th>AverageMarriageAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1990</td>\n",
       "      <td>24.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1991</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1992</td>\n",
       "      <td>24.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1993</td>\n",
       "      <td>24.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1994</td>\n",
       "      <td>25.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>United States</td>\n",
       "      <td>2011</td>\n",
       "      <td>26.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>United States</td>\n",
       "      <td>2002</td>\n",
       "      <td>25.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2001</td>\n",
       "      <td>30.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1997</td>\n",
       "      <td>29.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2011</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>290 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Entity  Year  AverageMarriageAge\n",
       "0        Australia  1990           24.299999\n",
       "1        Australia  1991           24.500000\n",
       "2        Australia  1992           24.700001\n",
       "3        Australia  1993           24.799999\n",
       "4        Australia  1994           25.100000\n",
       "..             ...   ...                 ...\n",
       "285  United States  2011           26.400000\n",
       "286  United States  2002           25.299999\n",
       "287         Sweden  2001           30.299999\n",
       "288         Sweden  1997           29.500000\n",
       "289    Switzerland  2011           29.900000\n",
       "\n",
       "[290 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql age_df << SELECT Entity, Year, AverageMarriageAge FROM age_raw_df GROUP BY Entity, Year, AverageMarriageAge\n",
    "%sql age_df << SELECT * FROM age_df WHERE Entity IN ('United States', 'United Kingdom', 'Greece', 'Sweden', 'Italy', 'Germany', 'Austria', 'Switzerland', 'Japan', 'Mexico', 'Canada', 'Australia')\n",
    "age_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5b520b",
   "metadata": {},
   "source": [
    "### Summary Statistics\n",
    "\n",
    "Like we did with our raw and cleaned marriage data, we once again used calc_summary_stats to find the mean, median, and standard deviation of our raw marriage age data (which included marriage ages from all 183 countries) as well as our cleaned marriage age data (which included the 9 countries we are interested in comparing). From our summary statistics, we can see that by focusing on the average marriage age for just the 9 countries, the median slightly increases by 2 years, the average increases slightly by 2 years, and the standard deviation decreases significantly by 1.1 years. Once again, the most significant change in statistic is the decrease in standard deviation– it indicates that after narrowing the amount of countries we're looking at, our data is less spread out and more clustered around the mean, resulting in more accurate estimates if we were to estimate a parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fc11f16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Marriage Age Data (All 181 Countries)\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 25.485271307493537\n",
      " Standard Deviation: 3.562861124219604\n",
      " Median: 25.6\n",
      "\n",
      "Cleaned Marriage Age Data (11 Specified Countries)\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 27.973793072413788\n",
      " Standard Deviation: 2.0688333956642997\n",
      " Median: 27.799999\n"
     ]
    }
   ],
   "source": [
    "print(\"Raw Marriage Age Data (All 181 Countries)\")\n",
    "calc_summary_stats(age_raw_df['AverageMarriageAge'])\n",
    "print(\"\\nCleaned Marriage Age Data (11 Specified Countries)\")\n",
    "calc_summary_stats(age_df['AverageMarriageAge'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e163d309",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Age for the 9 countries against the year.\n",
    "<br> <br>\n",
    "As seen from the graph above, global average marriage age has increased over time. Our dataset has the majority of data from each country from 1990 to 2020. We can see an increase in the range of the average marriage age: 21.5 to 27.5 in 1990 and 22.5 to 34 in 2020. However, the majority of the countries have a similar average marriage rate with an increase from 25 (1990) to 31 (2020). There is not a single country on our list which sought a decrease in the rate over time. Furthermore, Mexico seems to have the most rapid increase in the average marriage rate while United States seems to have the slowest increase in the age over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49381d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x2 = age_df['Year']\n",
    "y2 = age_df['AverageMarriageAge']\n",
    "seaborn.lineplot(data=age_df, x=x2, y=y2, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b7e38f",
   "metadata": {},
   "source": [
    "## Comparing Locations for Age and Marriage Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e957b24e",
   "metadata": {},
   "source": [
    "### United Kingdom, Italy, Germany\n",
    "Due to how these three countries are all in the same continent (Europe)  and is in a similar range in terms of population (UK: 67.22 million, Italy: 59.55 million, Germany: 83.24 million), we decided to analyze deeper into these three countries. \n",
    "\n",
    "### Data Cleaning \n",
    "For our first comparison for UK, Italy and Germany: Since we only we need data from the three countries, we decided to use SQL to select only the data on the three countries from the previously cleaned dataset. After plotting the age line plot, we had to consider that the year for the age dataset didn't start at the same time. Therefore, we decided to start the dataset at 1990 for the 3 countries for the age lineplot to make it easier to see the trend and analyze the data. This is done through trying to find whether there is a correlation between marriage rates and factors like age in countries in Europe with a similar population size."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f992a463",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Crude Marriage Rate for the 3 countries (United Kingdom, Italy, Germany) against Year\n",
    "<br> <br>\n",
    "The overall trend of crude marriage rate among the United Kingdom, Italy, and Germany has decreased over the time period between 1990 and 2015. United Kingdom and Germany has a similar trend and range, where they both decreased from 6.5 to around 5.25 as well as leveling off after 2000. However, Italy did not level off and continued to decrease nearly linearly from around 5.5 to 3.0 diverging from the three other countries. Although Italy and United Kingdom did not intersect on the graph, United Kingdom and Germany did a few times, especially haveing the same rate from 2005 to around 2007."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c8e9f9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable age_comp1\n",
      "Returning data to local variable new_df\n",
      "Returning data to local variable comparison1_df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%sql age_comp1 << SELECT * FROM age_df WHERE year >= 1990\n",
    "%sql new_df << SELECT * FROM marriage_df INNER JOIN age_comp1 ON marriage_df.year = age_comp1.year AND marriage_df.Entity = age_comp1.Entity\n",
    "new_df\n",
    "\n",
    "%sql comparison1_df << SELECT * FROM new_df WHERE Entity IN ('United Kingdom', 'Italy', 'Germany')\n",
    "marriage = comparison1_df['CrudeMarriageRate']\n",
    "age = comparison1_df['AverageMarriageAge']\n",
    "year = comparison1_df['Year']\n",
    "\n",
    "seaborn.lineplot(data=comparison1_df, x=year, y=marriage, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dcb2788",
   "metadata": {},
   "source": [
    "### Summary Statistics (United Kingdom vs. Italy vs. Germany)\n",
    "We looked at the summary statistics for the Average Marriage Age and Crudge Marriage Rate for United Kingdom, Italy, and Germany as a whole, as well as for the individual countries separately. The most significant trend to point out is that the standard deviation for Crude Marriage Rate of Germany is significantly lower than for the other 2 countries as well as the 3 countries as a whole. This indicates that if were to estimate a parameter for the Crude Marriage Rate of Germany, it would be the most accurate estimate of the three countries. Additionally, for average marriage age, we can see that Germany and Italy have equal and higher means than United Kingdom. For crude marriage rate, we can see that United Kingdom has the highest mean while Italy has the lowest mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "461897a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United Kingdom, Italy, and Germany:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 28.478666733333334\n",
      " Standard Deviation: 1.789397847619997\n",
      " Median: 28.4\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 4.843999997453334\n",
      " Standard Deviation: 0.6947882176606819\n",
      " Median: 4.9\n",
      "\n",
      "United Kingdom:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 28.42916691666667\n",
      " Standard Deviation: 1.7412827645080104\n",
      " Median: 28.5500005\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.079166658708333\n",
      " Standard Deviation: 0.6544585382262906\n",
      " Median: 5.0\n",
      "\n",
      "Italy:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 28.592307538461537\n",
      " Standard Deviation: 1.8605050211510241\n",
      " Median: 28.5\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 4.453846153846154\n",
      " Standard Deviation: 0.7474216034169989\n",
      " Median: 4.6\n",
      "\n",
      "Germany:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 28.408000119999997\n",
      " Standard Deviation: 1.753378466298804\n",
      " Median: 28.4\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.024\n",
      " Standard Deviation: 0.46067776156441503\n",
      " Median: 4.9\n"
     ]
    }
   ],
   "source": [
    "print(\"United Kingdom, Italy, and Germany:\")\n",
    "calc_summary_stats(comparison1_df['AverageMarriageAge'])\n",
    "calc_summary_stats(comparison1_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nUnited Kingdom:\")\n",
    "uk_df = comparison1_df.loc[comparison1_df['Entity'] == 'United Kingdom']\n",
    "calc_summary_stats(uk_df['AverageMarriageAge'])\n",
    "calc_summary_stats(uk_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nItaly:\")\n",
    "italy_df = comparison1_df.loc[comparison1_df['Entity'] == 'Italy']\n",
    "calc_summary_stats(italy_df['AverageMarriageAge'])\n",
    "calc_summary_stats(italy_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nGermany:\")\n",
    "germany_df = comparison1_df.loc[comparison1_df['Entity'] == 'Germany']\n",
    "calc_summary_stats(germany_df['AverageMarriageAge'])\n",
    "calc_summary_stats(germany_df['CrudeMarriageRate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab10b4b7",
   "metadata": {},
   "source": [
    "From the correlation between average marriage age and crude marriage rate for the the three countries, we can see that UK and Italy both have high correlations of -0.96 and -0.97 while Germany has a lower correlation of -0.75. A potential cause of this deviation is that although the three countries are all within similar proximities (located in Europe), Germany's population size is larger and deviates more from that of the other two countries (83 million vs. 59 million and 67 million). \n",
    "<br>\n",
    "This supports our hypothesis that countries within greater proximities will exhibit higher correlations between average marriage age and crude marriage rate. However, Germany's significantly lower correlation due to difference in population size may indicate that population size also has a large effect on the correlation between average marriage age and crude marriage rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7b7befb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Average Marriage Age and Crude Marriage Rate: \n",
      "\n",
      "UK:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.965451\n",
      "CrudeMarriageRate            -0.965451           1.000000\n",
      "\n",
      "Italy:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.976604\n",
      "CrudeMarriageRate            -0.976604           1.000000\n",
      "\n",
      "Germany:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.753943\n",
      "CrudeMarriageRate            -0.753943           1.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between Average Marriage Age and Crude Marriage Rate: \\n\")\n",
    "print(f\"UK: {uk_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")\n",
    "print(f\"Italy: {italy_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")\n",
    "print(f\"Germany: {germany_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dad283",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Age for the 3 countries (United Kingdom, Italy, Germany) against Year.\n",
    "<br> <br>\n",
    "The overall trend in average marriage age among the three countries has increased significantly from approximately 25.5 in 1990 to 32 in 2015. The three countries show a linear increase across the 25 years, where Italy and Germany diverged as Italy reveal a higher marriage rate after around 2010 compared to the other two countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1fbf918c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=comparison1_df, x=year, y=age, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c468c0",
   "metadata": {},
   "source": [
    "## Comparing Locations for Marriage Age and Marriage Rate\n",
    "\n",
    "### Japan v.s. Mexico\n",
    "Due to how these two countries have similar population numbers (Japan: 125.8 million, Mexico: 128.9 million), we wanted to see whether there is a correlation between the average marriage age and rate for these 2 countries.\n",
    "\n",
    "### Data Cleaning \n",
    "Since we only we need data for the 2 countries, we decided to use SQL to select only the data on the two countries from the previously cleaned dataset in order to find out whether there is a correlation between marriage rate and average age that people marry in countries with a similar population number but varies largely in culture due to its geography as Japan is in Asia and Mexico is in North America. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "662f57f5",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Rate against the Year for Japan and Mexico.\n",
    "<br> <br>\n",
    "Despite the similar population, Japan and Mexico had a significantly different crude marriage rate in 1995, 6.4 and 7.0 respectively. Mexico's crude marriage rate constantly decreased till 2008 which might be because of lack of data between 1995 and 2008. However, Japan's crude marriage rate fluctuated while it saw a significant decrease between 2000 and 2005. However, both countries confronted a similar trend starting from 2010 where Japan (around 5.1) ended up with a higher rate than Mexico (below 5.0) in 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a40fbff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable new2_df\n",
      "Returning data to local variable comparison2_df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%sql new2_df << SELECT * FROM marriage_df INNER JOIN age_comp1 ON marriage_df.year = age_comp1.year AND marriage_df.Entity = age_comp1.Entity\n",
    "new2_df\n",
    "%sql comparison2_df << SELECT * FROM new2_df WHERE Entity IN ('Japan', 'Mexico')\n",
    "comparison2_df\n",
    "marriage2 = comparison2_df['CrudeMarriageRate']\n",
    "age2 = comparison2_df['AverageMarriageAge']\n",
    "year2 = comparison2_df['Year']\n",
    "\n",
    "seaborn.lineplot(data=comparison2_df, x=year2, y=marriage2, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "224a6f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.proportion import proportions_ztest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c7be8cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable jap\n",
      "Returning data to local variable mex\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(   CrudeMarriageRate\n",
       " 0                4.9,\n",
       "    CrudeMarriageRate\n",
       " 0                4.3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding most recent marriage rate for both countries\n",
    "%sql jap << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Japan' AND Year = '2017'\n",
    "%sql mex << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Mexico' AND Year = '2017'\n",
    "jap, mex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a2f918",
   "metadata": {},
   "source": [
    "Sources: \n",
    "\n",
    "Population for Japan: https://www.worldometers.info/world-population/japan-population/\n",
    "\n",
    "Population for Mexico: https://www.worldometers.info/world-population/mexico-population/\n",
    "\n",
    "In terms of our source for the population of the countries we analyzed, we used worldometer. Worldometer is cited as a source in over 10,000 published books, in more than 6000 professional journal articles, and in over 1000 Wikipedia pages. In addition, Worldometer has gone through extensive research and data mining. It claims to base their data on the latest United Nations data, which is provided by the UNESCO Institute for Statistics found in the UIS Data Centre. They claim to strive for the highest level of accuracy. Therefore, we can assume that Worldometer is relatively pretty accurate. \n",
    "\n",
    "However, in terms of our data limitations, there have been reports of error in their data related to Covid-19 reportings. This raises the Worldometer’s methodological issues.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "083f64c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227.37290002584646, 0.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conducting z-test to compare both countries’ crude marriage rates\n",
    "marriage_num = [(127502725*.049),(124777324*.043)]\n",
    "population_size = [127502725, 124777324]\n",
    "z_calc, p_value = proportions_ztest(marriage_num, population_size)\n",
    "z_calc, p_value\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e5eaca",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "Our p-value of 0 is less than our sigificance level of 0.05, so we would reject the null hypothesis that the proportions (crude marriage rates) are the same for Japan and Mexico. We can conclude that the crude marriage rates for Japan and Mexico, which have similar population sizes, are significantly different."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32050ef4",
   "metadata": {},
   "source": [
    "### Summary Statistics (Japan vs. Mexico)\n",
    "The means of average marriage age for Japan and Mexico are fairly similar, with Mexico's mean being around 2 years younger than Japan's. Similarly, the median marriage age for Japan is higher than that of Mexico's, being around 1-2 years above. This shows how people in Japan tend to get married at an older age that people in Mexico. However Mexico's standard deviation in marriage age is higher than that of Japan, displaying how there is more variation in the age that people get married in Mexico compared to Japan throughout the years 1990-2020. \n",
    "\n",
    "Furthermore, mean crude marriage rate and median crude marriage rate in Japan are higher than those of Mexico's by around 0.60% and 0.85% respectively. This could suggest that a slightly higher portion of people in Japan get married compared to people in Mexico. The standard deviation of crude marriage rate in Mexico is a bit higher than that of Japan's, which portrays how there is, although not much, greater variation in the marriage rates for Mexico between the years 1990-2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04aa5925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Japan:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 27.692857107142853\n",
      " Standard Deviation: 1.2409467470774174\n",
      " Median: 27.6999995\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.803571428571429\n",
      " Standard Deviation: 0.4701687712613289\n",
      " Median: 5.85\n",
      "\n",
      "Mexico:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 25.61428557142857\n",
      " Standard Deviation: 2.3466867481361797\n",
      " Median: 26.299999\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.228571428571428\n",
      " Standard Deviation: 0.7264675505766354\n",
      " Median: 5.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Japan:\")\n",
    "japan_df = comparison2_df.loc[comparison2_df['Entity'] == 'Japan']\n",
    "calc_summary_stats(japan_df['AverageMarriageAge'])\n",
    "calc_summary_stats(japan_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nMexico:\")\n",
    "mexico_df = comparison2_df.loc[comparison2_df['Entity'] == 'Mexico']\n",
    "calc_summary_stats(mexico_df['AverageMarriageAge'])\n",
    "calc_summary_stats(mexico_df['CrudeMarriageRate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b618139",
   "metadata": {},
   "source": [
    "From the correlation between average marriage age and crude marriage rate for the the two countries, we can see that Japan and Mexico both have high correlations of -0.89 and -0.99. \n",
    "<br>\n",
    "This supports our hypothesis that countries with sith similar population numbers will exhibit higher correlations between average marriage age and crude marriage rate. However, Mexico seems to have a higher correlation rate since it has fewer data points to analyze compared to Japan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fd92ce5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Average Marriage Age and Crude Marriage Rate: \n",
      "\n",
      "Japan:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.893036\n",
      "CrudeMarriageRate            -0.893036           1.000000\n",
      "\n",
      "Mexico:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.989049\n",
      "CrudeMarriageRate            -0.989049           1.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between Average Marriage Age and Crude Marriage Rate: \\n\")\n",
    "print(f\"Japan: {japan_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")\n",
    "print(f\"Mexico: {mexico_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3370f3",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Age against the year for Japan and Mexico.\n",
    "<br> <br>\n",
    "The overall average marriage age between the two countries has increased constantly and significantly, especially in Mexico. Mexico's average marriage age started from 20 in 1995 and ended up near 27 in 2014. However, Japan's average marriage age started from around 26 in 1995 and ended up near 30 in 2014. We noticed that there was not much fluctuation in terms of average marriage age in both countries while witnessing faster increase in age in Mexico, however, still maintaining lower age compared to that of Japan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fdc42171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=comparison2_df, x=year2, y=age2, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49a6ce8",
   "metadata": {},
   "source": [
    "## Comparing Locations for Age and Marriage Rate\n",
    "\n",
    "### Austria v.s. Switzerland\n",
    "Due to how these two countries are in the same continent with a similar population number (Austria: 8.9 million, Switzerland: 8.6 million), we wanted to see whether there is a correlation between these 2 countries.\n",
    "\n",
    "### Data Cleaning \n",
    "Since we only we need data for the 2 countries, we decided to use SQL to select only the data on the two countries from the previously cleaned dataset in order to find out whether there is a correlation between marriage rate and average age that people marry in countries with a similar population number and in the same continent, which may lead to further research questions on whether there is a cultural similarity.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5128612e",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Crude Marriage Rate against the year for Austria and Switzerland.\n",
    "<br><br>\n",
    "Austria and Switzerland saw a similar trend from 1990 to 2005 in crude marriage rate. Both had moderate decreases in the rate from the beginning of the period to 1997, and there was a significant decrease in 2000. Then, both countries' rate increased till 2005 while Austria saw a significant decrease from 2005 to 2007. The most noticeable different is the trend from 2011 to 2020 where Austria's rate constantly increased upto the point where it outperformed Switzerland. On the other hand, Switzerland's rate maintained to decrease over the time period. Thus, from 2015, Autria outperformed Switzerland which led to a higher rate for Austria with around 5.2 and 5.0 for Switzerland in 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9f4b1ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable new3_df\n",
      "Returning data to local variable comparison3_df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%sql new3_df << SELECT * FROM marriage_df INNER JOIN age_comp1 ON marriage_df.year = age_comp1.year AND marriage_df.Entity = age_comp1.Entity\n",
    "%sql comparison3_df << SELECT * FROM new3_df WHERE Entity IN ('Austria', 'Switzerland')\n",
    "comparison3_df\n",
    "marriage3 = comparison3_df['CrudeMarriageRate']\n",
    "age3 = comparison3_df['AverageMarriageAge']\n",
    "year3 = comparison3_df['Year']\n",
    "\n",
    "seaborn.lineplot(data=comparison3_df, x=year3, y=marriage3, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "91218cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable swiss\n",
      "Returning data to local variable aus\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(   CrudeMarriageRate\n",
       " 0                4.8,\n",
       "    CrudeMarriageRate\n",
       " 0                4.8)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding most recent marriage rate for both countries\n",
    "%sql swiss << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Switzerland' AND Year = '2017'\n",
    "%sql aus << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Switzerland' AND Year = '2017'\n",
    "swiss, aus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc971f25",
   "metadata": {},
   "source": [
    "Sources:\n",
    "\n",
    "Population of Switzerland: https://www.worldometers.info/world-population/switzerland-population/\n",
    "\n",
    "Population of Austria: https://www.worldometers.info/world-population/austria-population/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1db89021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-28.727815184341274, 1.7148986325153763e-181)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conducting z-test to compare both countries’ crude marriage rates\n",
    "marriage_num = [(8455804*.048),(8819901*.051)]\n",
    "population_size = [8455804, 8819901]\n",
    "z_calc, p_value = proportions_ztest(marriage_num, population_size)\n",
    "z_calc, p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd215b1",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "Our p-value of 1.7148986325153763e-181 is less than our sigificance level of 0.05, so we would reject the null hypothesis that the proportions (crude marriage rates) are the same for Austria and Switzerland. We can conclude that the crude marriage rates for Austria and Switzerland, which have similar population sizes, are significantly different."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63888ead",
   "metadata": {},
   "source": [
    "### Summary Statistics (Austria vs. Switzerland)\n",
    "We looked at the summary statistics for the Average Marriage Age and Crude Marriage Rate for Austria and Switzerland separately. Looking at the statistics, we notice that mean and median for both average marriage age and crude marriage rate are higher in Switzerland. For instance, both countries have similar mean average marriage ages while the Switzerland’s median average marriage age is higher by about 1.5. From this, we can assume that Switzerland tends to have a higher average marriage age in terms of both mean and median. Furthermore, as the standard deviation is lower for Switzerland compared to that of Austria, we can assume that Switzerland is a better representation compared to Austria. On the other hand, the standard deviation for Switzerland’s crude marriage rate is higher than that of Austria, thus Austria might be a better estimate when analyzing crude marriage rate between the two countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "10334d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Austria:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 27.799999869565223\n",
      " Standard Deviation: 1.7487883681343817\n",
      " Median: 27.5\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 4.930434782608694\n",
      " Standard Deviation: 0.5295073860048115\n",
      " Median: 4.9\n",
      "\n",
      "Switzerland:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 28.800000038461537\n",
      " Standard Deviation: 1.1408498384302914\n",
      " Median: 29.05\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.546153846153846\n",
      " Standard Deviation: 0.5665216196775991\n",
      " Median: 5.4\n"
     ]
    }
   ],
   "source": [
    "print(\"Austria:\")\n",
    "austria_df = comparison3_df.loc[comparison3_df['Entity'] == 'Austria']\n",
    "calc_summary_stats(austria_df['AverageMarriageAge'])\n",
    "calc_summary_stats(austria_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nSwitzerland:\")\n",
    "switzerland_df = comparison3_df.loc[comparison3_df['Entity'] == 'Switzerland']\n",
    "calc_summary_stats(switzerland_df['AverageMarriageAge'])\n",
    "calc_summary_stats(switzerland_df['CrudeMarriageRate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7b74e4",
   "metadata": {},
   "source": [
    "From the correlation between average marriage age and crude marriage rate for the the two countries, we can see that Austria and Switzerland both have moderately high correlations of -0.76 and -0.83. \n",
    "<br>\n",
    "This supports our hypothesis that countries in the same continent with a similar population number will exhibit higher correlations between average marriage age and crude marriage rate. However, these rates are relatively lower than other countries' that we have discussed above since Autria and Switzerland's cultures are significantly different with noticeably different regulations, which might have led to moderately strong correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b428d8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Average Marriage Age and Crude Marriage Rate: \n",
      "\n",
      "Austria:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.757351\n",
      "CrudeMarriageRate            -0.757351           1.000000\n",
      "\n",
      "Switzerland:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.827768\n",
      "CrudeMarriageRate            -0.827768           1.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between Average Marriage Age and Crude Marriage Rate: \\n\")\n",
    "print(f\"Austria: {austria_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")\n",
    "print(f\"Switzerland: {switzerland_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395d3f7e",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Age against the year for Austria and Switzerland.\n",
    "<br><br>\n",
    "The overall trend in average marriage age in Austria and Switzerland has increased significantly from approximately 25 in 1990 to 31 in 2020. Both countries had constant increase in average marriage age over time where Austria has a steeper increase: Austria with an increase of 6 and 3 for Switzerland. Austria outperformed Switzerland starting from around 2007 which led Austria to end up with a higher average marriage age in 2015 with the result of over 31 while Switzerland with 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1c073cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=comparison3_df, x=year3, y=age3, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0615ee",
   "metadata": {},
   "source": [
    "## Comparing Locations for Age and Marriage Rate\n",
    "\n",
    "### Canada v.s. Australia\n",
    "Due to how these two countries has a similar population number (Canada: 38.01 million, Australia: 25.69 million), we wanted to see whether there is a correlation between these 2 countries.\n",
    "\n",
    "### Data Cleaning \n",
    "Since we only we need data for the 2 countries, we decided to use SQL to select only the data on the two countries from the previously cleaned dataset in order to find out whether there is a correlation between marriage rate and average age that people marry in countries with a similar population number despite its geographical location."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694a0eee",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Crude Marriage Rate against the year for Canada and Australia.\n",
    "<br><br>\n",
    "Looking at the graph, we can compare the trend in crude marriage rate between Canada and Australia from 1990 to around 2008 since we don't have any data after 2008 for Canada. Overall, the rate has decreased over time in both countries. Both started with a very similar rate in 1990 around 6.8. From then to 1996, there's a significant decrease in the rate. While Canada maintained the rate till 2000, Australia had an increase in 1998. Australia has a steady slow increase till 2008 after a drop in 2000. However, Canada's rate decreased after the drop in 2000. Even though the two countries started from a similar rate, they ended up at different rates in 2008: 5.5 for Australia and 4.3 for Canada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "611ac734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable new5_df\n",
      "Returning data to local variable comparison5_df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%sql new5_df << SELECT * FROM marriage_df INNER JOIN age_comp1 ON marriage_df.year = age_comp1.year AND marriage_df.Entity = age_comp1.Entity\n",
    "%sql comparison5_df << SELECT * FROM new5_df WHERE Entity IN ('Canada', 'Australia')\n",
    "comparison5_df\n",
    "marriage5 = comparison5_df['CrudeMarriageRate']\n",
    "age5 = comparison5_df['AverageMarriageAge']\n",
    "year5 = comparison5_df['Year']\n",
    "\n",
    "seaborn.lineplot(data=comparison5_df, x=year5, y=marriage5, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "93eaa19b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable canada\n",
      "Returning data to local variable australia\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(   CrudeMarriageRate\n",
       " 0                4.4,\n",
       "    CrudeMarriageRate\n",
       " 0                5.5)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding most recent marriage rate for both countries\n",
    "%sql canada << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Canada' AND Year = '2008'\n",
    "%sql australia << SELECT CrudeMarriageRate FROM marriage_df WHERE Entity = 'Australia' AND Year = '2008'\n",
    "canada, australia "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1266e93b",
   "metadata": {},
   "source": [
    "Sources:\n",
    "\n",
    "Population for Canada: https://www.worldometers.info/world-population/canada-population/\n",
    "\n",
    "Population for Australia: https://www.worldometers.info/world-population/australia-population/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e148dd1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-185.06068254015113, 0.0)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conducting z-test to compare both countries’ crude marriage rates\n",
    "marriage_num = [(33337638*.044),(21332282*.055)]\n",
    "population_size = [33337638, 21332282]\n",
    "z_calc, p_value = proportions_ztest(marriage_num, population_size)\n",
    "z_calc, p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc83289",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "Our p-value of 0 is less than our sigificance level of 0.05, so we would reject the null hypothesis that the proportions (crude marriage rates) are the same for Canada and Australia. We can conclude that the crude marriage rates for Canada and Australia, which have similar population sizes, are significantly different."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520694fa",
   "metadata": {},
   "source": [
    "### Summary Statistics (Canada vs. Australia)\n",
    "We looked at the summary statistics for the Average Marriage Age and Crude Marriage Rate for Canada and Australia as a whole, as well as, for the individual countries separately. Overall, Canada and Australia has a similar mean and median for both marriage rates and average age for marriage. However, because of how Canada has a slightly lower standard deviation for average marriage age, this could also indicate that Canada may be a more accurate estimate for marriage age between the two countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "479a060a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 27.342105421052633\n",
      " Standard Deviation: 1.1231141231052413\n",
      " Median: 27.200001\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.147368421052631\n",
      " Standard Deviation: 0.6081623926591279\n",
      " Median: 5.1\n",
      "\n",
      "Australia:\n",
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 26.903571464285715\n",
      " Standard Deviation: 1.3544852244357444\n",
      " Median: 27.3999995\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 5.639285714285715\n",
      " Standard Deviation: 0.544018963505006\n",
      " Median: 5.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Canada:\")\n",
    "canada_df = comparison5_df.loc[comparison5_df['Entity'] == 'Canada']\n",
    "calc_summary_stats(canada_df['AverageMarriageAge'])\n",
    "calc_summary_stats(canada_df['CrudeMarriageRate'])\n",
    "\n",
    "print(\"\\nAustralia:\")\n",
    "australia_df = comparison5_df.loc[comparison5_df['Entity'] == 'Australia']\n",
    "calc_summary_stats(australia_df['AverageMarriageAge'])\n",
    "calc_summary_stats(australia_df['CrudeMarriageRate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35c9a26",
   "metadata": {},
   "source": [
    "From the correlation between average marriage age and crude marriage rate for the the two countries, we can see that Canada and Australia both have high correlations of -0.92 and -0.95. \n",
    "<br>\n",
    "This supports our hypothesis that countries with similar population number will exhibit higher correlations between average marriage age and crude marriage rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "212c6e48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Average Marriage Age and Crude Marriage Rate: \n",
      "\n",
      "Canada:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.924502\n",
      "CrudeMarriageRate            -0.924502           1.000000\n",
      "\n",
      "Australia:                     AverageMarriageAge  CrudeMarriageRate\n",
      "AverageMarriageAge            1.000000          -0.945798\n",
      "CrudeMarriageRate            -0.945798           1.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between Average Marriage Age and Crude Marriage Rate: \\n\")\n",
    "print(f\"Canada: {canada_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")\n",
    "print(f\"Australia: {australia_df[['AverageMarriageAge', 'CrudeMarriageRate']].corr()}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a553a39",
   "metadata": {},
   "source": [
    "### Graph Description\n",
    "For this graph, we plotted the Average Marriage Age against the year for Canada and Australia.\n",
    "<br><br>\n",
    "The overall trend for both countries regarding average marriage age are very similar as both are increasing since 1990 to 2008. Canada seems to have a higher average marriage age from the start year 1990 to the end 2008. After around 2003, while Canada increases at a faster rate, Australia seems to increase at a slower rate after 2005."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "62f5df60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=comparison5_df, x=year5, y=age5, hue=\"Entity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2967a7",
   "metadata": {},
   "source": [
    "# United States\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7f7971",
   "metadata": {},
   "source": [
    "Due to the lack of data for other countries, we decided to focus on United States to delve deeper into identifying which factors have the strongest correlation with marriage rates. We used the same dataset for marriage and age. \n",
    "\n",
    "### Data Cleaning \n",
    "When using an inner join on us_marriage_df and us_age_df, it resulted in two year columns in us_df that had the same row values. To avoid any future issues or confusion from having multiple year columns, we renamed each year column to specify the data it came from (Marriage or Age)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9526f601",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable us_marriage_df\n",
      "Returning data to local variable us_marriage_df\n",
      "Returning data to local variable us_age_df\n",
      "Returning data to local variable us_age_df\n",
      "Returning data to local variable us_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarriageYear</th>\n",
       "      <th>CrudeMarriageRate</th>\n",
       "      <th>AgeYear</th>\n",
       "      <th>AverageMarriageAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>23.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1991</td>\n",
       "      <td>24.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1992</td>\n",
       "      <td>24.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1993</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1994</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1995</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1996</td>\n",
       "      <td>24.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1997</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1998</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1999</td>\n",
       "      <td>25.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2000</td>\n",
       "      <td>25.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2001</td>\n",
       "      <td>25.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2002</td>\n",
       "      <td>25.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2003</td>\n",
       "      <td>25.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2004</td>\n",
       "      <td>25.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2005</td>\n",
       "      <td>25.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2007</td>\n",
       "      <td>25.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2008</td>\n",
       "      <td>25.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2009</td>\n",
       "      <td>25.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2010</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2010</td>\n",
       "      <td>26.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2011</td>\n",
       "      <td>26.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2012</td>\n",
       "      <td>26.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2013</td>\n",
       "      <td>26.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2014</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2015</td>\n",
       "      <td>27.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>27.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2017</td>\n",
       "      <td>27.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2006</td>\n",
       "      <td>25.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MarriageYear  CrudeMarriageRate  AgeYear  AverageMarriageAge\n",
       "0           1990                9.8     1990           23.900000\n",
       "1           1991                9.4     1991           24.100000\n",
       "2           1992                9.3     1992           24.400000\n",
       "3           1993                9.0     1993           24.500000\n",
       "4           1994                9.1     1994           24.500000\n",
       "5           1995                8.9     1995           24.500000\n",
       "6           1996                8.8     1996           24.799999\n",
       "7           1997                8.9     1997           25.000000\n",
       "8           1998                8.4     1998           25.000000\n",
       "9           1999                8.6     1999           25.100000\n",
       "10          2000                8.3     2000           25.100000\n",
       "11          2001                8.2     2001           25.100000\n",
       "12          2002                7.8     2002           25.299999\n",
       "13          2003                7.7     2003           25.299999\n",
       "14          2004                7.8     2004           25.299999\n",
       "15          2005                7.6     2005           25.299999\n",
       "16          2007                7.3     2007           25.600000\n",
       "17          2008                7.1     2008           25.900000\n",
       "18          2009                6.8     2009           25.900000\n",
       "19          2010                6.8     2010           26.100000\n",
       "20          2011                6.8     2011           26.400000\n",
       "21          2012                6.8     2012           26.600000\n",
       "22          2013                6.8     2013           26.600000\n",
       "23          2014                6.9     2014           27.000000\n",
       "24          2015                6.9     2015           27.100000\n",
       "25          2016                7.0     2016           27.400000\n",
       "26          2017                6.9     2017           27.400000\n",
       "27          2006                7.5     2006           25.500000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql us_marriage_df << SELECT * FROM marriage_raw_df WHERE Entity = 'United States'\n",
    "%sql us_marriage_df << SELECT Year, CrudeMarriageRate FROM us_marriage_df GROUP BY year, CrudeMarriageRate\n",
    "us_marriage_df = us_marriage_df.rename(columns={'Year': 'MarriageYear'})\n",
    "#print(us_marriage_df)\n",
    "\n",
    "%sql us_age_df << SELECT * FROM age_raw_df WHERE Entity = 'United States' \n",
    "%sql us_age_df << SELECT Year, AverageMarriageAge FROM us_age_df GROUP BY Year, AverageMarriageAge\n",
    "us_age_df[\"Year\"] = us_age_df[\"Year\"].astype(int)\n",
    "us_age_df = us_age_df.rename(columns={'Year': 'AgeYear'})\n",
    "#print(us_age_df)\n",
    "\n",
    "%sql us_df << SELECT * FROM us_marriage_df INNER JOIN us_age_df ON us_marriage_df.MarriageYear = us_age_df.AgeYear \n",
    "#us_df = us_df.rename(columns={'MarriageYear': 'Year'})\n",
    "us_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0b08edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Category</th>\n",
       "      <th>Net worth</th>\n",
       "      <th>Assets</th>\n",
       "      <th>Real estate</th>\n",
       "      <th>Consumer durables</th>\n",
       "      <th>Corporate equities and mutual fund shares</th>\n",
       "      <th>DB pension entitlements</th>\n",
       "      <th>DC pension entitlements</th>\n",
       "      <th>Private businesses</th>\n",
       "      <th>Other assets</th>\n",
       "      <th>Liabilities</th>\n",
       "      <th>Home mortgages</th>\n",
       "      <th>Consumer credit</th>\n",
       "      <th>Other liabilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989:Q3</td>\n",
       "      <td>TopPt1</td>\n",
       "      <td>1762754</td>\n",
       "      <td>1808118</td>\n",
       "      <td>143483</td>\n",
       "      <td>198277</td>\n",
       "      <td>297188</td>\n",
       "      <td>10305</td>\n",
       "      <td>28192</td>\n",
       "      <td>599209</td>\n",
       "      <td>531464</td>\n",
       "      <td>45363</td>\n",
       "      <td>15660</td>\n",
       "      <td>20152</td>\n",
       "      <td>9551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989:Q3</td>\n",
       "      <td>RemainingTop1</td>\n",
       "      <td>2847464</td>\n",
       "      <td>2928475</td>\n",
       "      <td>452975</td>\n",
       "      <td>161152</td>\n",
       "      <td>570202</td>\n",
       "      <td>109570</td>\n",
       "      <td>74104</td>\n",
       "      <td>725055</td>\n",
       "      <td>835418</td>\n",
       "      <td>81012</td>\n",
       "      <td>62513</td>\n",
       "      <td>4856</td>\n",
       "      <td>13643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989:Q3</td>\n",
       "      <td>Next9</td>\n",
       "      <td>7614607</td>\n",
       "      <td>8115723</td>\n",
       "      <td>2070067</td>\n",
       "      <td>417078</td>\n",
       "      <td>814534</td>\n",
       "      <td>1411757</td>\n",
       "      <td>425100</td>\n",
       "      <td>1016995</td>\n",
       "      <td>1960192</td>\n",
       "      <td>501116</td>\n",
       "      <td>397307</td>\n",
       "      <td>56293</td>\n",
       "      <td>47516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989:Q3</td>\n",
       "      <td>Next40</td>\n",
       "      <td>7421340</td>\n",
       "      <td>8996174</td>\n",
       "      <td>3415352</td>\n",
       "      <td>760975</td>\n",
       "      <td>345482</td>\n",
       "      <td>1787955</td>\n",
       "      <td>424008</td>\n",
       "      <td>582316</td>\n",
       "      <td>1680086</td>\n",
       "      <td>1574834</td>\n",
       "      <td>1160943</td>\n",
       "      <td>363894</td>\n",
       "      <td>49997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989:Q3</td>\n",
       "      <td>Bottom50</td>\n",
       "      <td>773613</td>\n",
       "      <td>1721722</td>\n",
       "      <td>810861</td>\n",
       "      <td>358869</td>\n",
       "      <td>24313</td>\n",
       "      <td>158378</td>\n",
       "      <td>56403</td>\n",
       "      <td>46051</td>\n",
       "      <td>266847</td>\n",
       "      <td>948110</td>\n",
       "      <td>574267</td>\n",
       "      <td>339011</td>\n",
       "      <td>34832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2022:Q2</td>\n",
       "      <td>TopPt1</td>\n",
       "      <td>17054366</td>\n",
       "      <td>17184481</td>\n",
       "      <td>1463136</td>\n",
       "      <td>432591</td>\n",
       "      <td>7153298</td>\n",
       "      <td>45845</td>\n",
       "      <td>141999</td>\n",
       "      <td>5033798</td>\n",
       "      <td>2913813</td>\n",
       "      <td>130115</td>\n",
       "      <td>60569</td>\n",
       "      <td>18713</td>\n",
       "      <td>50833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>2022:Q2</td>\n",
       "      <td>RemainingTop1</td>\n",
       "      <td>25079978</td>\n",
       "      <td>25754269</td>\n",
       "      <td>3874882</td>\n",
       "      <td>489192</td>\n",
       "      <td>10325857</td>\n",
       "      <td>435177</td>\n",
       "      <td>842315</td>\n",
       "      <td>4611284</td>\n",
       "      <td>5175562</td>\n",
       "      <td>674290</td>\n",
       "      <td>526101</td>\n",
       "      <td>41339</td>\n",
       "      <td>106850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>2022:Q2</td>\n",
       "      <td>Next9</td>\n",
       "      <td>50035271</td>\n",
       "      <td>53695141</td>\n",
       "      <td>12898784</td>\n",
       "      <td>1592273</td>\n",
       "      <td>11645344</td>\n",
       "      <td>8841753</td>\n",
       "      <td>5503839</td>\n",
       "      <td>4129079</td>\n",
       "      <td>9084069</td>\n",
       "      <td>3659870</td>\n",
       "      <td>3041273</td>\n",
       "      <td>303026</td>\n",
       "      <td>315571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>2022:Q2</td>\n",
       "      <td>Next40</td>\n",
       "      <td>39175173</td>\n",
       "      <td>46868279</td>\n",
       "      <td>16946726</td>\n",
       "      <td>3232344</td>\n",
       "      <td>3601795</td>\n",
       "      <td>8611730</td>\n",
       "      <td>4622638</td>\n",
       "      <td>2231056</td>\n",
       "      <td>7621990</td>\n",
       "      <td>7693106</td>\n",
       "      <td>5692757</td>\n",
       "      <td>1636552</td>\n",
       "      <td>363797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>2022:Q2</td>\n",
       "      <td>Bottom50</td>\n",
       "      <td>4405938</td>\n",
       "      <td>10256405</td>\n",
       "      <td>6012657</td>\n",
       "      <td>1927205</td>\n",
       "      <td>204702</td>\n",
       "      <td>324980</td>\n",
       "      <td>559555</td>\n",
       "      <td>137503</td>\n",
       "      <td>1089802</td>\n",
       "      <td>5850466</td>\n",
       "      <td>2838703</td>\n",
       "      <td>2582367</td>\n",
       "      <td>429396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Category  Net worth    Assets  Real estate  \\\n",
       "0    1989:Q3         TopPt1    1762754   1808118       143483   \n",
       "1    1989:Q3  RemainingTop1    2847464   2928475       452975   \n",
       "2    1989:Q3          Next9    7614607   8115723      2070067   \n",
       "3    1989:Q3         Next40    7421340   8996174      3415352   \n",
       "4    1989:Q3       Bottom50     773613   1721722       810861   \n",
       "..       ...            ...        ...       ...          ...   \n",
       "655  2022:Q2         TopPt1   17054366  17184481      1463136   \n",
       "656  2022:Q2  RemainingTop1   25079978  25754269      3874882   \n",
       "657  2022:Q2          Next9   50035271  53695141     12898784   \n",
       "658  2022:Q2         Next40   39175173  46868279     16946726   \n",
       "659  2022:Q2       Bottom50    4405938  10256405      6012657   \n",
       "\n",
       "     Consumer durables  Corporate equities and mutual fund shares  \\\n",
       "0               198277                                     297188   \n",
       "1               161152                                     570202   \n",
       "2               417078                                     814534   \n",
       "3               760975                                     345482   \n",
       "4               358869                                      24313   \n",
       "..                 ...                                        ...   \n",
       "655             432591                                    7153298   \n",
       "656             489192                                   10325857   \n",
       "657            1592273                                   11645344   \n",
       "658            3232344                                    3601795   \n",
       "659            1927205                                     204702   \n",
       "\n",
       "     DB pension entitlements  DC pension entitlements  Private businesses  \\\n",
       "0                      10305                    28192              599209   \n",
       "1                     109570                    74104              725055   \n",
       "2                    1411757                   425100             1016995   \n",
       "3                    1787955                   424008              582316   \n",
       "4                     158378                    56403               46051   \n",
       "..                       ...                      ...                 ...   \n",
       "655                    45845                   141999             5033798   \n",
       "656                   435177                   842315             4611284   \n",
       "657                  8841753                  5503839             4129079   \n",
       "658                  8611730                  4622638             2231056   \n",
       "659                   324980                   559555              137503   \n",
       "\n",
       "     Other assets  Liabilities  Home mortgages  Consumer credit  \\\n",
       "0          531464        45363           15660            20152   \n",
       "1          835418        81012           62513             4856   \n",
       "2         1960192       501116          397307            56293   \n",
       "3         1680086      1574834         1160943           363894   \n",
       "4          266847       948110          574267           339011   \n",
       "..            ...          ...             ...              ...   \n",
       "655       2913813       130115           60569            18713   \n",
       "656       5175562       674290          526101            41339   \n",
       "657       9084069      3659870         3041273           303026   \n",
       "658       7621990      7693106         5692757          1636552   \n",
       "659       1089802      5850466         2838703          2582367   \n",
       "\n",
       "     Other liabilities  \n",
       "0                 9551  \n",
       "1                13643  \n",
       "2                47516  \n",
       "3                49997  \n",
       "4                34832  \n",
       "..                 ...  \n",
       "655              50833  \n",
       "656             106850  \n",
       "657             315571  \n",
       "658             363797  \n",
       "659             429396  \n",
       "\n",
       "[660 rows x 15 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networth_df = pd.read_csv('dfa-networth-levels.csv') \n",
    "networth_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25036cc9",
   "metadata": {},
   "source": [
    "### Data Cleaning: networth_df\n",
    "In order to clean the data, we changed the name of Net Worth column from \"Net worth\" to \"NetWorth\" to avoid any SQL errors.  We also selected only the date and the networth column from the database. This is because the only necessary data we need are the year, which is used to join with the previous database and the net worth, which is the factor that we're trying to analyze and compare. The other columns are irrelevant to our research question and so it wasn't necessary to include them.\n",
    "<br><br>\n",
    "We wanted to clean the \"Date\" column from our data by removing the colon and Quarter number so that we could rename the column as year and work with years rather than dates. We've been mainly working with years, so we wanted to keep it consistent and also easier to analyze the overall data. In order to clean the column, we first converted the data type of the column from 'object' to 'string'. Because there are only four possible quarters, rather than using regex patterns, we used the string replace method to replace all possible quarters with an empty string.\n",
    "\n",
    "### Data Description: networth_df\n",
    "Lastly, in our cleaned networth_df, we get the observation of Total Net Worth to the according year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9af2e7c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable networth_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>TotalNetWorth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1989</td>\n",
       "      <td>41219904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>84550037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1991</td>\n",
       "      <td>89315232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1992</td>\n",
       "      <td>93644532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993</td>\n",
       "      <td>99456291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1994</td>\n",
       "      <td>103630172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1995</td>\n",
       "      <td>110548363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1996</td>\n",
       "      <td>118946458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1997</td>\n",
       "      <td>129297278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1998</td>\n",
       "      <td>143407083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1999</td>\n",
       "      <td>155502039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000</td>\n",
       "      <td>168806078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2001</td>\n",
       "      <td>171239312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2002</td>\n",
       "      <td>174391336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2003</td>\n",
       "      <td>184183159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2004</td>\n",
       "      <td>209604049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2005</td>\n",
       "      <td>231403406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2006</td>\n",
       "      <td>250661000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2007</td>\n",
       "      <td>263224001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2008</td>\n",
       "      <td>245040207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2009</td>\n",
       "      <td>229402906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010</td>\n",
       "      <td>242044848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011</td>\n",
       "      <td>252772592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012</td>\n",
       "      <td>265125538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>291861434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014</td>\n",
       "      <td>318387875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015</td>\n",
       "      <td>334423014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016</td>\n",
       "      <td>349775078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>376060766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>397007933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019</td>\n",
       "      <td>424905717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>454336686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2021</td>\n",
       "      <td>542462599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2022</td>\n",
       "      <td>277465151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  TotalNetWorth\n",
       "0   1989       41219904\n",
       "1   1990       84550037\n",
       "2   1991       89315232\n",
       "3   1992       93644532\n",
       "4   1993       99456291\n",
       "5   1994      103630172\n",
       "6   1995      110548363\n",
       "7   1996      118946458\n",
       "8   1997      129297278\n",
       "9   1998      143407083\n",
       "10  1999      155502039\n",
       "11  2000      168806078\n",
       "12  2001      171239312\n",
       "13  2002      174391336\n",
       "14  2003      184183159\n",
       "15  2004      209604049\n",
       "16  2005      231403406\n",
       "17  2006      250661000\n",
       "18  2007      263224001\n",
       "19  2008      245040207\n",
       "20  2009      229402906\n",
       "21  2010      242044848\n",
       "22  2011      252772592\n",
       "23  2012      265125538\n",
       "24  2013      291861434\n",
       "25  2014      318387875\n",
       "26  2015      334423014\n",
       "27  2016      349775078\n",
       "28  2017      376060766\n",
       "29  2018      397007933\n",
       "30  2019      424905717\n",
       "31  2020      454336686\n",
       "32  2021      542462599\n",
       "33  2022      277465151"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networth_df = networth_df.rename(columns={\"Net worth\" : \"NetWorth\", \"Date\": \"Year\"})\n",
    "networth_df['Year'] = networth_df['Year'].astype(pd.StringDtype())\n",
    "# networth_df['Year'].dtypes\n",
    "networth_df[\"Year\"] = networth_df['Year'].str.replace(\":Q1\", \"\")\n",
    "networth_df[\"Year\"] = networth_df['Year'].str.replace(\":Q2\", \"\")\n",
    "networth_df[\"Year\"] = networth_df['Year'].str.replace(\":Q3\", \"\")\n",
    "networth_df[\"Year\"] = networth_df['Year'].str.replace(\":Q4\", \"\")\n",
    "%sql networth_df << SELECT Year, SUM(NetWorth) AS TotalNetWorth FROM networth_df GROUP BY Year\n",
    "networth_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d044b4",
   "metadata": {},
   "source": [
    "### Data Cleaning: outside_birth_df \n",
    "\n",
    "In order to clean the data, we changed the name of Births outside of marriage column from \"Births outside of marriage (% of all births)\" to \"BirthsOutsideOfMarriagePercentage\" to avoid any SQL errors.  \n",
    "\n",
    "In terms of data cleaning, the \"code\" column was removed because it wasn't relevant for data analysis/exploration as it was redundant. \n",
    "\n",
    "The dataset had 42 different countries which are much lesser than our dataset for crude marriage rate and average marriage age. Thus, we limited our outside birth data to United States to further dive in to possible relationships of outside birth percentage with marriage rate and cohabitant data. Since the entity would just be \"United States\", it is redundant for our table and so we decided to remove that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f0d2482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>BirthsOutsideOfMarriagePercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>1971</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>1976</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>1979</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>1980</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2010</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2011</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2013</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2014</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1875 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Entity Code  Year  BirthsOutsideOfMarriagePercentage\n",
       "0         Australia  AUS  1971                                9.3\n",
       "1         Australia  AUS  1974                                9.5\n",
       "2         Australia  AUS  1976                               10.1\n",
       "3         Australia  AUS  1979                               11.7\n",
       "4         Australia  AUS  1980                               12.4\n",
       "...             ...  ...   ...                                ...\n",
       "1870  United States  USA  2010                               40.8\n",
       "1871  United States  USA  2011                               40.7\n",
       "1872  United States  USA  2012                               40.7\n",
       "1873  United States  USA  2013                               40.6\n",
       "1874  United States  USA  2014                               40.2\n",
       "\n",
       "[1875 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outside_birth_raw_df = pd.read_csv('share-of-births-outside-marriage.csv')\n",
    "outside_birth_raw_df = outside_birth_raw_df.rename(columns={\"Births outside of marriage (% of all births)\" : \"BirthsOutsideOfMarriagePercentage\"})\n",
    "outside_birth_raw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6202f0d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique values 42\n"
     ]
    }
   ],
   "source": [
    "unique_vals = len(pd.unique(outside_birth_raw_df['Entity']))\n",
    "print(\"No. of unique values\", unique_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f230ec2",
   "metadata": {},
   "source": [
    "We renamed the 'Year' column as 'BirthYear' column in order to specify the data that our Year column is coming from. We did this so that later when we join our outside birth data together with our marriage and net worth data, it is easier to remove any duplicate year columns and keep one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b4f6e91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable outside_birth_df\n",
      "Returning data to local variable outside_birth_df\n",
      "Returning data to local variable outside_birth_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>BirthsOutsideOfMarriagePercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1965</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1966</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1967</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1968</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1970</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1971</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1972</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1973</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1975</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1976</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1977</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1978</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1979</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1980</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1981</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1982</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1983</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1984</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1985</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1986</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1987</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1988</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1990</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1991</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1992</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1993</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1994</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1999</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2001</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2002</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2003</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2004</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2005</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2007</td>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2008</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2009</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2011</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2012</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2013</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2014</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BirthYear  BirthsOutsideOfMarriagePercentage\n",
       "0        1960                                5.3\n",
       "1        1961                                5.6\n",
       "2        1962                                5.9\n",
       "3        1963                                6.3\n",
       "4        1964                                6.9\n",
       "5        1965                                7.7\n",
       "6        1966                                8.4\n",
       "7        1967                                9.0\n",
       "8        1968                                9.7\n",
       "9        1969                               10.0\n",
       "10       1970                               10.7\n",
       "11       1971                               11.3\n",
       "12       1972                               12.4\n",
       "13       1973                               13.0\n",
       "14       1974                               13.2\n",
       "15       1975                               14.3\n",
       "16       1976                               14.8\n",
       "17       1977                               15.5\n",
       "18       1978                               16.3\n",
       "19       1979                               17.1\n",
       "20       1980                               18.4\n",
       "21       1981                               18.9\n",
       "22       1982                               19.4\n",
       "23       1983                               20.3\n",
       "24       1984                               21.0\n",
       "25       1985                               22.0\n",
       "26       1986                               23.4\n",
       "27       1987                               24.5\n",
       "28       1988                               25.7\n",
       "29       1989                               27.1\n",
       "30       1990                               28.0\n",
       "31       1991                               29.5\n",
       "32       1992                               30.1\n",
       "33       1993                               31.0\n",
       "34       1994                               32.6\n",
       "35       1995                               32.2\n",
       "36       1996                               32.4\n",
       "37       1997                               32.4\n",
       "38       1998                               32.8\n",
       "39       1999                               33.0\n",
       "40       2000                               33.2\n",
       "41       2001                               33.5\n",
       "42       2002                               34.0\n",
       "43       2003                               34.6\n",
       "44       2004                               35.8\n",
       "45       2005                               36.9\n",
       "46       2006                               38.5\n",
       "47       2007                               39.7\n",
       "48       2008                               40.6\n",
       "49       2009                               41.0\n",
       "50       2010                               40.8\n",
       "51       2011                               40.7\n",
       "52       2012                               40.7\n",
       "53       2013                               40.6\n",
       "54       2014                               40.2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql outside_birth_df << SELECT Entity, Year, BirthsOutsideOfMarriagePercentage FROM outside_birth_raw_df GROUP BY Entity, Year, BirthsOutsideOfMarriagePercentage\n",
    "%sql outside_birth_df << SELECT * FROM outside_birth_df WHERE Entity IN ('United States')\n",
    "%sql outside_birth_df << SELECT Year, BirthsOutsideOfMarriagePercentage FROM outside_birth_df ORDER BY Year ASC\n",
    "outside_birth_df = outside_birth_df.rename(columns={'Year': 'BirthYear'})\n",
    "outside_birth_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b465c0ea",
   "metadata": {},
   "source": [
    "## Data Cleaning: cohabitation_df\n",
    "\n",
    "We downloaded data on U.S. cohabitation rates as an Excel file (xls). Then, we converted it into a csv file. We created a pandas dataframe from the livingarrangements.csv file. Then, We renamed all the columns to focus on the column we was interested in which we renamed as the\" \"Percent_num\" - the percentage of U.S. adults who live with their partners (cohabitation rate). Then, we created an SQL file from the existing pandas dataframe, further filtering out the data by selecting only the Year and cohabitation rates (Percent_num) column where there is data. To further filter out the data, using SQL, we selected only data from the first 32 rows of from the column of interest, where we got the data for cohabitation rates from the years 1990-2021. Since the file was converted from a xls file to a csv file, some letters were altered, causing one of the Years to be written down as \"2014s\". Therefore, using the pandas dataframe, we replaced the incorrect value of '2014s' to '2014' with help from https://www.askpython.com/python-modules/pandas/update-the-value-of-a-row-dataframe . As a result, we got the final pandas dataframe for cohabitation rates in the U.S. from years 1990-2021 saved as 'cohabitation'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d64ebb6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable cohabitation_df\n",
      "Returning data to local variable cohabitation_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohabitationYear</th>\n",
       "      <th>CohabitationPercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1990</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1991</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1992</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1993</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1994</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1995</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1996</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1997</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1998</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2001</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2002</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2003</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2004</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2005</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cohabitationYear CohabitationPercentage\n",
       "31              1990                    3.1\n",
       "30              1991                    3.2\n",
       "29              1992                    3.5\n",
       "28              1993                    3.7\n",
       "27              1994                    3.9\n",
       "26              1995                    3.7\n",
       "25              1996                    3.0\n",
       "24              1997                    3.3\n",
       "23              1998                    3.3\n",
       "22              1999                    3.5\n",
       "21              2000                    3.9\n",
       "20              2001                    4.1\n",
       "19              2002                    4.2\n",
       "18              2003                    4.6\n",
       "17              2004                    4.6\n",
       "16              2005                    4.7\n",
       "15              2006                    4.8\n",
       "14              2007                    6.3\n",
       "13              2008                    6.5\n",
       "12              2009                    6.3\n",
       "11              2010                    7.1\n",
       "10              2011                    7.1\n",
       "9               2012                    7.3\n",
       "8               2013                    7.5\n",
       "7               2014                    7.3\n",
       "6               2015                    7.5\n",
       "5               2016                    7.4\n",
       "4               2017                    7.1\n",
       "3               2018                    7.7\n",
       "2               2019                    7.4\n",
       "1               2020                    7.3\n",
       "0               2021                    8.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohabitation_raw_df = pd.read_csv('livingarrangements.csv')\n",
    "\n",
    "cohabitation_raw_df.columns=['Year',\"1\",'2','3','4','5','6','7','8','9','CohabitationPercentage','11','12','13','14','15','16','7','8','9','10','11','12','13','14','15','16','7','8','9','10','11','12','13','14']\n",
    "\n",
    "\n",
    "%sql cohabitation_df << SELECT Year, CohabitationPercentage FROM cohabitation_raw_df WHERE CohabitationPercentage IS NOT NULL\n",
    "\n",
    "%sql cohabitation_df << SELECT Year, CohabitationPercentage FROM cohabitation_df LIMIT 32 OFFSET 1;\n",
    "\n",
    "cohabitation_df.at[7,'Year']= 2014\n",
    "\n",
    "cohabitation_df = cohabitation_df.rename(columns = {'Year': 'cohabitationYear'})\n",
    "\n",
    "cohabitation_df.cohabitationYear = cohabitation_df.cohabitationYear.astype(int)\n",
    "cohabitation_df = cohabitation_df.sort_values(by='cohabitationYear', ascending=True)\n",
    "\n",
    "cohabitation_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb424b4",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "Because the inner join resulted in four different year columns (MarriageYear, AgeYear, CohabitationYear, BirthYear, Year) that all had the same values, we wanted to condense this into just one year column to avoid any confusion or future issues. Because we previously renamed the year columns from us_df to distinguish them, this made it easy to drop the columns we wanted to. We chose to drop these columns because the MarriageYear was our first column making the data easier to read, and we renamed the MarriageYear column as Year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "03ccd74d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning data to local variable us_df\n",
      "Returning data to local variable us_df\n",
      "Returning data to local variable us_df\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>CrudeMarriageRate</th>\n",
       "      <th>AverageMarriageAge</th>\n",
       "      <th>TotalNetWorth</th>\n",
       "      <th>BirthsOutsideOfMarriagePercentage</th>\n",
       "      <th>CohabitationPercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>9.8</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>84550037</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>9.4</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>89315232</td>\n",
       "      <td>29.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>9.3</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>93644532</td>\n",
       "      <td>30.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>99456291</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>9.1</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>103630172</td>\n",
       "      <td>32.6</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>8.9</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>110548363</td>\n",
       "      <td>32.2</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>8.8</td>\n",
       "      <td>24.799999</td>\n",
       "      <td>118946458</td>\n",
       "      <td>32.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>129297278</td>\n",
       "      <td>32.4</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>8.4</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>143407083</td>\n",
       "      <td>32.8</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>8.6</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>155502039</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>168806078</td>\n",
       "      <td>33.2</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>8.2</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>171239312</td>\n",
       "      <td>33.5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.8</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>174391336</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.7</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>184183159</td>\n",
       "      <td>34.6</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.8</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>209604049</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>7.6</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>231403406</td>\n",
       "      <td>36.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.5</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>250661000</td>\n",
       "      <td>38.5</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.3</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>263224001</td>\n",
       "      <td>39.7</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008</td>\n",
       "      <td>7.1</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>245040207</td>\n",
       "      <td>40.6</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2009</td>\n",
       "      <td>6.8</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>229402906</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010</td>\n",
       "      <td>6.8</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>242044848</td>\n",
       "      <td>40.8</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011</td>\n",
       "      <td>6.8</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>252772592</td>\n",
       "      <td>40.7</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>6.8</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>265125538</td>\n",
       "      <td>40.7</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>6.8</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>291861434</td>\n",
       "      <td>40.6</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.9</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>318387875</td>\n",
       "      <td>40.2</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  CrudeMarriageRate  AverageMarriageAge  TotalNetWorth  \\\n",
       "0   1990                9.8           23.900000       84550037   \n",
       "1   1991                9.4           24.100000       89315232   \n",
       "2   1992                9.3           24.400000       93644532   \n",
       "3   1993                9.0           24.500000       99456291   \n",
       "4   1994                9.1           24.500000      103630172   \n",
       "5   1995                8.9           24.500000      110548363   \n",
       "6   1996                8.8           24.799999      118946458   \n",
       "7   1997                8.9           25.000000      129297278   \n",
       "8   1998                8.4           25.000000      143407083   \n",
       "9   1999                8.6           25.100000      155502039   \n",
       "10  2000                8.3           25.100000      168806078   \n",
       "11  2001                8.2           25.100000      171239312   \n",
       "12  2002                7.8           25.299999      174391336   \n",
       "13  2003                7.7           25.299999      184183159   \n",
       "14  2004                7.8           25.299999      209604049   \n",
       "15  2005                7.6           25.299999      231403406   \n",
       "16  2006                7.5           25.500000      250661000   \n",
       "17  2007                7.3           25.600000      263224001   \n",
       "18  2008                7.1           25.900000      245040207   \n",
       "19  2009                6.8           25.900000      229402906   \n",
       "20  2010                6.8           26.100000      242044848   \n",
       "21  2011                6.8           26.400000      252772592   \n",
       "22  2012                6.8           26.600000      265125538   \n",
       "23  2013                6.8           26.600000      291861434   \n",
       "24  2014                6.9           27.000000      318387875   \n",
       "\n",
       "    BirthsOutsideOfMarriagePercentage  CohabitationPercentage  \n",
       "0                                28.0                     3.1  \n",
       "1                                29.5                     3.2  \n",
       "2                                30.1                     3.5  \n",
       "3                                31.0                     3.7  \n",
       "4                                32.6                     3.9  \n",
       "5                                32.2                     3.7  \n",
       "6                                32.4                     3.0  \n",
       "7                                32.4                     3.3  \n",
       "8                                32.8                     3.3  \n",
       "9                                33.0                     3.5  \n",
       "10                               33.2                     3.9  \n",
       "11                               33.5                     4.1  \n",
       "12                               34.0                     4.2  \n",
       "13                               34.6                     4.6  \n",
       "14                               35.8                     4.6  \n",
       "15                               36.9                     4.7  \n",
       "16                               38.5                     4.8  \n",
       "17                               39.7                     6.3  \n",
       "18                               40.6                     6.5  \n",
       "19                               41.0                     6.3  \n",
       "20                               40.8                     7.1  \n",
       "21                               40.7                     7.1  \n",
       "22                               40.7                     7.3  \n",
       "23                               40.6                     7.5  \n",
       "24                               40.2                     7.3  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql us_df << SELECT * FROM us_df INNER JOIN networth_df ON us_df.MarriageYear = networth_df.year\n",
    "us_df.drop('AgeYear', inplace=True, axis=1)\n",
    "us_df.drop('Year', inplace=True, axis=1)\n",
    "us_df = us_df.rename(columns = {'MarriageYear': 'Year'})\n",
    "%sql us_df << SELECT * FROM us_df INNER JOIN outside_birth_df ON us_df.Year = outside_birth_df.BirthYear\n",
    "us_df.drop('BirthYear', inplace=True, axis=1)\n",
    "%sql us_df << SELECT * FROM us_df INNER JOIN cohabitation_df ON us_df.Year = cohabitation_df.cohabitationYear\n",
    "us_df.drop('cohabitationYear', inplace=True, axis=1)\n",
    "us_df['CohabitationPercentage'] = us_df['CohabitationPercentage'].astype(float)\n",
    "us_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8d8e3d",
   "metadata": {},
   "source": [
    "### Summary Statistics (United States)\n",
    "The mean average marriage age for the U.S. is around 25.3 years, while the median is around the same. Therefore, this could suggest that the U.S. population tend to get married at around 25.3 years. However the standard deviation of the marriage age is almost 1 year, which displays how much the data could vary for the years 1990-2020. \n",
    "\n",
    "The average crude marriage rate for the U.S. is around 8%, while the median is around the same. This suggests that around 8% of the U.S. population gets married during the years 1990-2014. However, there is almost a 1% standard deviation among the marriage rates in the U.S., showing how the marriage rates could vary around 1% throughout the years in the dataset (1990-2020).\n",
    "\n",
    "The average and median total net worth for the U.S. are around 170,000,000 dollars - 180,000,000 dollars while the standard deviation is up to $69,267,186. This suggests that there is a huge income inequality issue in the U.S., which could cause the data correlation between marriage rates and wealth to be biased. \n",
    "\n",
    "The mean cohabitation rate in the U.S. is around 5 percent while the median is around 4 percent, which is a fairly small percentage. However, the standard deviation is up to around 1.54 percent, suggesting that there is great variations or shifts throughout the years 1990-2014 for cohabitation rates in the U.S. which we could further explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "878452d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Stats for AverageMarriageAge:\n",
      " Mean: 25.311999800000002\n",
      " Standard Deviation: 0.7911106400498986\n",
      " Median: 25.299999\n",
      "Summary Stats for CrudeMarriageRate:\n",
      " Mean: 8.016000042080002\n",
      " Standard Deviation: 0.9443219200492355\n",
      " Median: 7.800000191\n",
      "Summary Stats for TotalNetWorth:\n",
      " Mean: 185057809.04\n",
      " Standard Deviation: 69267186.17706585\n",
      " Median: 174391336.0\n",
      "Summary Stats for BirthsOutsideOfMarriagePercentage:\n",
      " Mean: 35.39200000000001\n",
      " Standard Deviation: 4.103649107806368\n",
      " Median: 34.0\n",
      "Summary Stats for CohabitationPercentage:\n",
      " Mean: 4.819999999999999\n",
      " Standard Deviation: 1.5401298646542765\n",
      " Median: 4.2\n"
     ]
    }
   ],
   "source": [
    "calc_summary_stats(us_df['AverageMarriageAge'])\n",
    "calc_summary_stats(us_df['CrudeMarriageRate'])\n",
    "calc_summary_stats(us_df['TotalNetWorth'])\n",
    "calc_summary_stats(us_df['BirthsOutsideOfMarriagePercentage'])\n",
    "calc_summary_stats(us_df['CohabitationPercentage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bb869f",
   "metadata": {},
   "source": [
    "## Graph Description  \n",
    "\n",
    "In order to identify the correlations between the marriage rates and the factors to answer our research questions, we decided to plotting graphs displaying each factor plotted against year. As seen from the graphs below, there is a strong negative correlation between the average age people got married in the US and the marriage rates in the US. However, for the total net worth, there is a variation in the networth and as a result, has no correlation with both the average age and the marriage rates. \n",
    "On the other hand, we can see moderately positive relationships among the average marriage age, total net worth, birth outside of marriage, and cohabitation where they all increase over time. Especially, there is a stronger moderate positive relationships between birth outside of marriage and cohabitation where the trend of increase and decrease in following results seem similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e985503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "marriage_us = us_df['CrudeMarriageRate']\n",
    "age_us = us_df['AverageMarriageAge']\n",
    "year_us = us_df['Year']\n",
    "networth_us = us_df['TotalNetWorth']\n",
    "outsidebirth_us = us_df['BirthsOutsideOfMarriagePercentage']\n",
    "cohabitation_us = us_df['CohabitationPercentage']\n",
    "\n",
    "seaborn.lineplot(data=us_df, x=year_us, y=marriage_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07f608c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AverageMarriageAge'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rg2imIa20F8/pvw68Ej5ERBKycNNexr2/jpsGdmPIKRrSqg3imWp3sJnNM7N8Mysys6NmllcTxYlI7RIMaS2mQ4tGPDRUZ2nVFvEcbH8GuAnIARoDXwWejrIoEamdRs/MYW1uAY9efzbNG9VPdjlSTeIanHT3NWZW192PAhPN7MOI6xKRWmbR5n2MeW8twwd05cLemha7NoknSA6GsxQuMrPHgO1A02jLEpHa5HBxMKTVvkUjHvqcztKqbeIZ2hoB1CWYsbCAYGKr66MsSkRql9+8lUPOznx+8aWzaKEhrVonnrO2NoaLh4CfRFuOiNQ2S7bs4w/vrmNY/y5cfGq7ZJcjESg3SMzsZXe/0cyWEs5FEsvdz460MhFJe8GQ1hLaNGvAD6/pk+xyJCIV9UjuC39eUxOFiEjtceToMV5duJWn385h855DTLgti5aNNaRVW5UbJO6+3czqAuPd/bIarElE0lTx0WO8+u+tPP2vNWzac5CzOrfkZ7efyUUa0qrVKjxG4u5HzeygmbV0d02vKyJlKj56jNcWbePpf+WwcfdBzuzcgnG3ZnHp6e00j3oGiOf030JgqZnNIDhrCwB3vzeyqkQkLRQfPca0xdt4+l9rWL+rgDM6tWDsrVlcpgDJKPEEyd/DR5WYWVeCGzx2IJjTZIy7P2VmjwB3AbnhWx9y9+lltN8AHACOAsXunlXVGkQkGkePOdMWb+Xpt9awblcBp3dswbMj+nNFn/YKkAxUYZCEx0hGHOcxkmLggXDO9+bAgrBXA/Ckuz8ex2dc7O67juO7RSQCR485ry/ZxlNv5bAut4DTOjTnD18JAqROHQVIporsGIm7bye4Ch53P2Bm2UDn4y9VRJJp+/5D3D5xHis/PsCp7Zvz+1vO5cozOihApGaOkZhZd6AfMIdgXpN7zOxWYD5Br2VvGc0c+KeZOfCsu48p57NHAaMAunXrFm9JIlIFH+8vZPiY2ezJL+KZm/sx9MyOChD5hAXTjVTwBrORZa1390lxfYFZM+Bd4OfuPjWc830XQVD8DOjo7neU0a6Tu28zs3bADOCb7v5eRd+VlZXl8+fPj6csEYlTECIfsSu/iD/eOZBzu7VKdklSjcxsQaLHoOO5RUpcgVEWM6sPTAEmu/vU8PN2xLw+Fni9nO/dFv7caWavAgOBCoNERKrXx/sLuWnsbHblFzHpDoWIlC2eia16mdkrZrbCzNaVPOJoZ8B4INvdn4hZ3zHmbV8ElpXRtml4gJ5wbvgrynqfiERnR14hN4+dzc68QibdMYD+JylEpGzxHCOZCPwYeJJgjvbbCeZur8wQgjsHLzWzReG6h4CbzOwcgqGtDcD/QDCUBYxz96FAe+DV8DTCesAL7v5GXFskIgnbmVfITWNmsyOvkEl3DKT/SScmuyRJYfEcI1ng7v3NbKm7nxWue9/dL6iRCqtAx0hEErfzQHBg/eP9QYgM6K4Qqc1q5BgJUGhmdYAcM7sH2AroxjkitdDOA0FPRCEiVRHPxFb3A02Ae4H+BMNVZZ7JJSLpK/fAYW4eO4ft+wt57naFiMQvnrO25oWL+QTHR0SklglCZDZb9x7iudsHMLCHQkTiV9HEVtMqaujun6/+ckSkpu3KD0Jky95DTLx9AINObp3skiTNVNQjOQ/YDLxIcEW6LmMVqWVKQmTz3oNMvG0ggxUichwqCpIOwOXATcDNBHcAftHdl9dEYSK1xZGjx9idX5TsMv5LQVEx33h+IZv2HGTCbQM4r6dCRI5PRTMkHgXeAN4ws4YEgfKOmf3U3Z+uqQJF0tnOvEJuHjeHNTvzk11KmRrVr8OEkQM4v2ebZJciaayy28g3BD5HECLdgd8AU6MvSyT97cwrZPjY2ezYX8jD1/ShSYO6yS7pv/TrdgKndWiR7DIkzVV0sH0ScCbwD+An7q5blIjEaeeB4B5Vuh5DMkFFPZIRBLeN7w3cF97OHYKD7u7u+jVGpAy6HkMyTUXHSOK5WFFEYuh6DMlEcYWFmX3GzG4Pl9uYWY9oyxJJP7oeQzJVPLeR/zHwPeD74aoGwPNRFiWSbmKvx5hw2wBdjyEZJZ4eyReBzxNOsxtOONU8yqJE0snu/MPcMnaOrseQjBVPkBR5cK95h08mmhIRwhAZN4eNewp0PYZkrHiC5GUzexY4wczuAmYCY6MtSyT17Sko4pZxc1i/q4DxIwdw/ikKEclM8dz993EzuxzIA04FfuTuMyKvTCSF7S0VIkMUIpLB4pnYijA4FB4ifBoia3PzGT8yi8/0UohIZqs0SMzsAOHxkRj7gfnAA+6+LorCRKpq8eZ9bNhdEOl3uMPY99exJjefcbdmcUGvtpF+n0g6iKdH8gSwDXiB4Kr24QR3Bl4FTAAuiqo4kXgs2LiH0TNzeD9nV418X8N6dRh7axYX9laIiEB8QXKVuw+KeT7GzGa7+0/N7KGoChOpzMJNe3lyxmrez9lF66YNeGjoaVx6evvIJ845sWkDTmjSIOJvEUkf8QTJMTO7EXglfH5DzGulh7xEIvfvTXsZPTOHd1fncmLTBjx49Wncet5JNGkQ1yE/Ealm8fzLuwV4CvgdQXDMBr5iZo2BeyKsTeQ/LNq8j9EzV/POqlxaNanP964KAqRpQwWISDLFc/rvOuDacl6eVV47M+sK/JHgeMoxYIy7P2VmjwB3AbnhWx9y9+lltL+KIMDqAuPc/dHKapXaacmWfYyemcO/Vu7khCb1+c6VpzLy/O40U4CIpIR4ztpqBNwJnAE0Klnv7ndU0rSY4KyuhWbWHFhgZiWnED/p7o9X8J11gd8STPW7BZhnZtPcfUVl9UrtsXTLfkbPXM1bChCRlBbPv8g/ASuBK4GfEgx1ZVfWyN23A9vD5QNmlg10jrOugcCaklOLzewl4DpAQZKGPlyzi3tf+jf7Dx2pUrsjR52Wjevz7St6M/L87jRvVD+iCkUkEfEEySnuPszMrnP3SWb2AvBmVb7EzLoD/YA5wBDgHjO7lU+vRdlbqklnYHPM8y3AIMpgZqOAUQDdunWrSllSAz5cs4s7Js2ja6sm3JjVtUptWzdryLCsLrRQgIiktHiCpOTXyH1mdibwMcH87XExs2bAFOB+d88zs98DPyM4cP8z4NdA6WGyss7gLPMMMXcfA4wByMrK0llkKeTDtUGInHRiU164axCtmzVMdkkiEoF4gmSMmbUCfghMA5oBD8fz4WZWnyBEJrv7VAB33xHz+ljg9TKabgFif33tQnBRpKSJj9bu5o7n5tHtxCZMVoiI1GoVBomZ1QHywqGn94CT4/1gMzNgPJDt7k/ErO8YHj+BYK6TZWU0nwf0Cmdi3EpwNf3N8X63JNfsdUGIdG3VhBfuGkwbhYhIrVbhbeTd/RjHf63IEGAEcImZLQofQ4HHzGypmS0BLga+BWBmncxsevi9xeH3vklwYP9ld19+nHVIDZqzbje3T5xH51aNFSIiGcKCOasqeIPZw8Ah4M+EsyQCuPueaEuruqysLJ8/f36yy8hYc9fv4baJc+nYshEvjhpMu+aNKm8kIkllZgvcPSuRz4jnGEnJgfC7Y9Y5VRjmktpv3gaFiEimiufK9h41UYikr/kb9nDbhLl0aNmIF+9SiIhkmkqn2jWzJmb2QzMbEz7vZWbXRF+apIMFG/cwcsJc2rdoxEt3DaZdC4WISKaJZ872iUARcH74fAvwf5FVJGljwca9jJwwj3YtwuEshYhIRoonSHq6+2OEFya6+yHKvmBQMsjCTXsZOWEubZo14MW7BtNeISKSseIJkqLwlvEOYGY9gcORViUp7d+b9jJy/FxaN2vAi6MG06GlQkQkk8Vz1tYjwBtAVzObTHB9yG0R1iQpbFf+Ye6cNJ8TmzXgpVGD6diycbJLEpEki+esrX+a2QJgMMGQ1n3uXjOTY0vK+fFfl5NfWKwQEZFPxDMfyTTgRWCauxdU9n6pvaYv3c7fl27nO1eeSu/2zZNdjoikiHiOkfwauABYYWZ/MbMbwsmuJIPsKSji4deWcVbnlvzPhboWVUQ+Fc/Q1rvAu+GshZcQTJM7AWgRcW2SQn48bTl5hUeYPGwQ9erG8/uHiGSKuOYsDc/auhb4MnAu8FyENUmKeWPZx/xt8Tb+9/LenNZBvz+IyH+K58r2PxPcgfcS4BlgJFA34rokRewtKOKHry2jT8cWfP2inskuR0RSULxXtg8D8sLlnxDHnO1SO/zkb8vZd7CIx4f1pb6GtESkDOUObZlZb4IJpW4CdhPcRt7c/eIaqk2SbMaKHby2aBv3X9aLPp00pCUiZavoGMlK4H3gWndfA2Bm36qRqiTp9h0s4qFXl3Jah+Z846JTkl2OiKSwisYqrgc+Bt42s7Fmdim6x1bG+OnrK9hTEAxpNainIS0RKV+5/0O4+6vu/mXgNOAdgilx25vZ783sihqqT5LgrewdTF24lbsv6smZnVsmuxwRSXGV/qrp7gXuPtndrwG6AIuAB6MuTJJj/6Ejnwxp3XNJr2SXIyJpoEpjFu6+x92fdfdLoipIkuv/Xl/BrvwifnWDhrREJD76n0I+8faqnfxlwRa+9tmTOauLhrREJD4KEgEgr/AI35+ylF7tmnHvpRrSEpH4RRYkZtbVzN42s2wzW25m95V6/dtm5mbWppz2G8xsqZktMrP5UdUpgV/8PZudBwp5fFhfGtbTjQtEJH5x3WvrOBUDD7j7QjNrDiwwsxnuvsLMugKXA5sq+YyLNfdJ9N5bnctL8zbztc/2pG/XE5JdjoikmciCxN23A9vD5QNmlg10BlYATwLfBf4a1ffXBoVHjpJXeCTS7zh85BgPTllCz7ZNuf8yDWmJSNVF2SP5hJl1B/oBc8zs88BWd19sVuH1jQ7808wceNbdx0RfaWrYd7CI8bPWM/GDDeQfLo78++oYvPL182lUX0NaIlJ1kQeJmTUDpgD3Ewx3/QCI54LGIe6+zczaATPMbKW7v1fG548CRgF069at2upOhv0HjzB+1jomfrCBA4eL+dxZHRncs3XktxM4tUNzzu3WKuJvEZHaKtIgMbP6BCEy2d2nmtlZQA+gpDfSBVhoZgPd/ePYtu6+Lfy508xeBQYC/xUkYU9lDEBWVpZHuT1R2X/oSNADmbWeA4eLGXpWB+69tJfm/hCRtBBZkFiQFOOBbHd/AsDdlwLtYt6zAcgqfUDdzJoCdcJjK00JejA/jarWZMkrPMKEWesZP2s9BwqLueqMDtx3WS9O76gAEZH0EWWPZAgwAlhqZovCdQ+5+/Sy3mxmnYBx7j4UaA+8GvZa6gEvuPsbEdZao/IKjzBx1gbGz1pHXmExV57Rnnsv7cUZnXQRoIiknyjP2ppFJXcLdvfuMcvbgKHh8jqgb1S1JcuBwiM898EGxs1az/5DR7i8T3vuu7SXbowoImmtRs7aEnhj2Xa+N2Up+w8d4bLT23P/ZQoQEakdFCQ1YOu+Q3z7L0vo0aYpv/jiWbqPlYjUKgqSiLk735+6lGPu/O6Wc+l6YpNklyQiUq1008aI/WXBFt5bncv3rjpNISIitZKCJELb9x/iZ6+vYGCPExkx+KRklyMiEgkFSUTcnYemLuXI0WP86oazqVNH092LSO2kIInI1IVbeXtVMKR1UuumyS5HRCQyCpII7Mgr5Cd/W86A7q0YeV73ZJcjIhIpBUk1KxnSOlx8jMdu6KshLRGp9RQk1ey1RVt5a+VOvnPlqfRooyEtEan9FCTVaOeBQh6ZtoJzu53A7UN6JLscEZEaoSCpJu7OD15dxqEjR/nVsL7U1ZCWiGQIBUk1mbZ4GzNW7ODbV/SmZ9tmyS5HRKTGKEiqQe6BwzwybTnndD2BOz9zcrLLERGpUQqSBLk7D7+2jIKiozw+7GwNaYlIxlGQJOjvS7fzxvKP+dZlvTmlXfNklyMiUuMUJAnYnX+YH/11OX27tOSuC3SWlohkJgVJAn40bTn5hcU8dkNf6tXVH6WIZCb973ecpi/dzt+XbOe+y3pxagcNaYlI5lKQHIedBwp5+LVlnNm5BaMu1FlaIpLZFCRVlHvgMDePncPBoqM8Pqwv9TWkJSIZTv8LVsGu/MPcPHY2W/ceYuLtAzitQ4tklyQiknQKkjiVhMjmvQeZcNsABp/cOtkliYikhMiCxMy6mtnbZpZtZsvN7L5Sr3/bzNzM2pTT/iozW2Vma8zswajqjMfu/MPcMnYOm/YEIXJeT4WIiEiJKHskxcAD7n46MBi428z6QBAywOXAprIamlld4LfA1UAf4KaStjVtd/5hbhk3hw27C5gwcgDn9ywz90REMlZkQeLu2919Ybh8AMgGOocvPwl8F/Bymg8E1rj7OncvAl4Crouq1vLsKSjilnFzWL+rgAm3DeD8UxQiIiKl1cgxEjPrDvQD5pjZ54Gt7r64giadgc0xz7fwaQjViL0xITJ+5ACGKERERMpUL+ovMLNmwBTgfoLhrh8AV1TWrIx1ZfZezGwUMAqgW7dux11nrL0FRdw8bg5rc/MZd2sWn+mlEBERKU+kPRIzq08QIpPdfSrQE+gBLDazDUAXYKGZdSjVdAvQNeZ5F2BbWd/h7mPcPcvds9q2bZtwzfsOBj2RkhC5sHfinykiUptF1iMxMwPGA9nu/gSAuy8F2sW8ZwOQ5e67SjWfB/Qysx7AVmA4cHNUtZYoCZE1ufmMVYiIiMQlyh7JEGAEcImZLQofQ8t7s5l1MrPpAO5eDNwDvElwkP5ld18eYa3sP3iEr4yfQ86OfMaM6M9nFSIiInGJrEfi7rMo+1hH7Hu6xyxvA4bGPJ8OTI+qvlglIbL643yeHdGfi05tV3kjEREBdGU7BwqPMGLCHFZ9fIA/jDiXi09TiIiIVEXkZ22lukb163Jym6bcd2kvLjmtfbLLERFJOxkfJPXr1mH08H7JLkNEJG1l/NCWiIgkRkEiIiIJUZCIiEhCFCQiIpIQBYmIiCREQSIiIglRkIiISEIUJCIikhBzL2+SwvRjZrnAxuNs3gYofRfiTJHJ2w6Zvf3a9sxVsv0nuXtCd6mtVUGSCDOb7+5Zya4jGTJ52yGzt1/bnpnbDtW7/RraEhGRhChIREQkIQqST41JdgFJlMnbDpm9/dr2zFVt269jJCIikhD1SEREJCEKEhERSUitDRIzm2BmO81sWcy6vmb2kZktNbO/mVmLcH0DM5sYrl9sZhfFtOkfrl9jZr8xswrnoU8V1bj975jZKjNbFD5Sfi5iM+tqZm+bWbaZLTez+8L1J5rZDDPLCX+2imnz/XAfrzKzK2PWp9X+r+ZtT6t9X9VtN7PW4fvzzeyZUp+VVvsdqn37q7bv3b1WPoALgXOBZTHr5gGfDZfvAH4WLt8NTAyX2wELgDrh87nAeYAB/wCuTva21fD2vwNkJXt7qrjtHYFzw+XmwGqgD/AY8GC4/kHgl+FyH2Ax0BDoAawF6qbj/q/mbU+rfX8c294U+AzwNeCZUp+VVvs9gu2v0r6vtT0Sd38P2FNq9anAe+HyDOD6cLkP8FbYbiewD8gys45AC3f/yIM/3T8CX4i28upRHdsffZXRcPft7r4wXD4AZAOdgeuASeHbJvHpvrwOeMndD7v7emANMDAd9391bXuNFl1Nqrrt7l7g7rOAwtjPScf9DtW3/cej1gZJOZYBnw+XhwFdw+XFwHVmVs/MegD9w9c6A1ti2m8J16Wrqm5/iYlh9/bhdOjixzKz7kA/YA7Q3t23Q/CPjqD3BcE+3RzTrGQ/p/X+T3DbS6Tlvo9z28uT1vsdEt7+EnHv+0wLkjuAu81sAUHXryhcP4HgL8t8YDTwIVBM0K0tLZ3Pl67q9gPc4u5nAReEjxE1WXAizKwZMAW4393zKnprGeu8gvUprxq2HdJ031dh28v9iDLWpcV+h2rZfqjivs+oIHH3le5+hbv3B14kGA/G3Yvd/Vvufo67XwecAOQQ/OfaJeYjugDbarjsanMc24+7bw1/HgBeIE2GPcysPsE/psnuPjVcvSMctigZvtgZrt/Cf/bASvZzWu7/atr2tNz3Vdz28qTlfodq2/4q7/uMCpKSMw/MrA7wQ+AP4fMmZtY0XL4cKHb3FWE38ICZDQ67drcCf01O9Ymr6vaHQ11twvX1gWsIhsdSWrivxgPZ7v5EzEvTgJHh8kg+3ZfTgOFm1jAc2usFzE3H/V9d256O+/44tr1M6bjfofq2/7j2fTLPMojyQfAb93bgCMFvGHcC9xGcybAaeJRPr+zvDqwiODg1k+C2yiWfkxX+Ia4Fnilpk+qP6th+grM6FgBLgOXAU4Rn9KTyg+BMFA/rXhQ+hgKtCU4qyAl/nhjT5gfhPl5FzBk66bb/q2vb03HfH+e2byA4KSU//HfSJx33e3Vu//Hse90iRUREEpJRQ1siIlL9FCQiIpIQBYmIiCREQSIiIglRkIiISEIUJCLHyQKzzOzqmHU3mtkbyaxLpKbp9F+RBJjZmcBfCO5rVJfg3P2r3H3tcXxWXXc/Wr0VikRPQSKSIDN7DCgguJCrADgJOAuoBzzi7n8Nb6L3p/A9APe4+4cWzP3yY4KLR89x9z41W71I4hQkIgkKby+zkOAmmK8Dy939eTM7gWBei34EVxwfc/dCM+sFvOjuWWGQ/B0404PbuIuknXrJLkAk3bl7gZn9meA2EzcC15rZt8OXGwHdCG7694yZnQMcBXrHfMRchYikMwWJSPU4Fj4MuN7dV8W+aGaPADuAvgQnucROJlRQQzWKREJnbYlUrzeBb5ZMBGRm/cL1LYHt7n6MYG6HukmqT6TaKUhEqtfPgPrAEjNbFj4H+B0w0sxmEwxrqRcitYYOtouISELUIxERkYQoSEREJCEKEhERSYiCREREEqIgERGRhChIREQkIQoSERFJyP8HLgGe4YlfiMgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=us_df, x=year_us, y=age_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d28befb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='TotalNetWorth'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=us_df, x=year_us, y=networth_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9132a5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='BirthsOutsideOfMarriagePercentage'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=us_df, x=year_us, y=outsidebirth_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05403509",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='CohabitationPercentage'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.lineplot(data=us_df, x=year_us, y=cohabitation_us)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0ea8b2",
   "metadata": {},
   "source": [
    "## Correlation Matrix \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35370a3",
   "metadata": {},
   "source": [
    "In order to further understand the correlation between marriage rate and the other factors we are analyzing, we decided to use a correlation matrix. We then chose to display the correlation matrix using a heatmap or color-encoded matrix so it is easier to visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2963811b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = us_df[['CrudeMarriageRate', 'AverageMarriageAge', 'TotalNetWorth', 'BirthsOutsideOfMarriagePercentage', 'CohabitationPercentage']]\n",
    "corr = X.corr()\n",
    "seaborn.heatmap(corr, annot=True)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88a9a9f",
   "metadata": {},
   "source": [
    "From the correlation matrix, we can see that there are really strong correlations between marriage rates and the factors that were used as well as strong correlations between the variables itself. \n",
    "\n",
    "### Hypothesis 3 Results\n",
    "We predicted that wealth would be the factor that shows the strongest correlation with marriage rates in the United States. We can see that wealth and marriage rates have a correlation of -0.95, which is a strong negative correlation. However, wealth isn't the factor that shows the strongest correlation as births outside of marriage percentage and marriage rates show an even stronger correlation (-0.97). This was rather unexpected because we believed that wealth was a huge contribution or consideration into marriage but rather the births outside of marriage shows a stronger correlation. \n",
    "\n",
    "### Hypothesis 7 Results\n",
    "We predicted that there could be a correlation between the factors. We believe that there could be a strong correlation between cohabitation and birth outside of marriage and a strong correlation betwen age and wealth. We found that age and wealth has a strong correlation of 0.94. This makes sense logically because people who are single could be more wealthy due to no distractions such as starting a family or wedding plannings or being wealthier could be raising their standards of a *perfect* partner so they marry later in life when they want to settle down. In terms of cohabitation and birth outside of marriage, there is also a strong correlation of 0.94. This is expected because living together as unmarried partners could also mean getting pregnant outside of marriage. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb237d89",
   "metadata": {},
   "source": [
    "## Train/Test Split \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6fe16aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "us_train, us_test = train_test_split(us_df, test_size = 0.1, random_state = 2950) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e664b33f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient is [-3.15401846e-01 -1.51859380e-09 -1.72884946e-01  8.15359342e-02]\n",
      "The intercept is 22.026492904214944\n"
     ]
    }
   ],
   "source": [
    "list_strings = ['AverageMarriageAge', 'TotalNetWorth', 'BirthsOutsideOfMarriagePercentage', 'CohabitationPercentage']\n",
    "train = us_train\n",
    "test = us_test\n",
    "predict = 'CrudeMarriageRate'\n",
    "\n",
    "x = train[list_strings]\n",
    "y = train[predict]\n",
    "\n",
    "\n",
    "model = LinearRegression().fit(x, y)\n",
    "\n",
    "predict_train = model.predict(train[list_strings])\n",
    "predict_test = model.predict(test[list_strings])\n",
    "\n",
    "print(f\"The coefficient is {model.coef_}\")\n",
    "print(f\"The intercept is {model.intercept_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b958fb",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b859ff0a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageMarriageAge</th>\n",
       "      <th>TotalNetWorth</th>\n",
       "      <th>BirthsOutsideOfMarriagePercentage</th>\n",
       "      <th>CohabitationPercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AverageMarriageAge</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944833</td>\n",
       "      <td>0.931268</td>\n",
       "      <td>0.928895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalNetWorth</th>\n",
       "      <td>0.944833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.948161</td>\n",
       "      <td>0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BirthsOutsideOfMarriagePercentage</th>\n",
       "      <td>0.931268</td>\n",
       "      <td>0.948161</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohabitationPercentage</th>\n",
       "      <td>0.928895</td>\n",
       "      <td>0.908046</td>\n",
       "      <td>0.938628</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   AverageMarriageAge  TotalNetWorth  \\\n",
       "AverageMarriageAge                           1.000000       0.944833   \n",
       "TotalNetWorth                                0.944833       1.000000   \n",
       "BirthsOutsideOfMarriagePercentage            0.931268       0.948161   \n",
       "CohabitationPercentage                       0.928895       0.908046   \n",
       "\n",
       "                                   BirthsOutsideOfMarriagePercentage  \\\n",
       "AverageMarriageAge                                          0.931268   \n",
       "TotalNetWorth                                               0.948161   \n",
       "BirthsOutsideOfMarriagePercentage                           1.000000   \n",
       "CohabitationPercentage                                      0.938628   \n",
       "\n",
       "                                   CohabitationPercentage  \n",
       "AverageMarriageAge                               0.928895  \n",
       "TotalNetWorth                                    0.908046  \n",
       "BirthsOutsideOfMarriagePercentage                0.938628  \n",
       "CohabitationPercentage                           1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_corr = us_train[['AverageMarriageAge', 'TotalNetWorth', 'BirthsOutsideOfMarriagePercentage', 'CohabitationPercentage']]\n",
    "us_corr.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705f9394",
   "metadata": {},
   "source": [
    "### Dropping a Dummy Variable \n",
    "\n",
    "Multicollinearity is when there are high correlations between predictor/independent variables with one or more independent variables in a multiple regression equation. It can create bias and noise in our predictions and dataset, undermining the significance of the predictor/independent variable. As seen above, there is a high correlation between Total Net Worth and the other three predictor variables (0.94 with average marriage rate, 0.95 with births outside marriage percentage and 0.91 with cohabitation percentage). Therefore, to avoid multicollinearity, we decided to drop the dummy variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a6905a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient is [-0.38147595 -0.18728805  0.08690366]\n",
      "The intercept is 23.902841911404593\n"
     ]
    }
   ],
   "source": [
    "list_strings = ['AverageMarriageAge', 'BirthsOutsideOfMarriagePercentage'  , 'CohabitationPercentage']\n",
    "train = us_train\n",
    "test = us_test\n",
    "predict = 'CrudeMarriageRate'\n",
    "\n",
    "x = train[list_strings]\n",
    "y = train[predict]\n",
    "\n",
    "\n",
    "model = LinearRegression().fit(x, y)\n",
    "\n",
    "predict_train = model.predict(train[list_strings])\n",
    "predict_test = model.predict(test[list_strings])\n",
    "\n",
    "print(f\"The coefficient is {model.coef_}\")\n",
    "print(f\"The intercept is {model.intercept_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5719be1f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ec432706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AverageMarriageAge', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=us_df['AverageMarriageAge'], y=us_df['CrudeMarriageRate'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "20f12f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trying to fit into exponential model cuz looks like it would fit better\n",
    "#p = np.polyfit(x=us_df['AverageMarriageAge'], y=np.log(us_df['CrudeMarriageRate']), deg=1)\n",
    "p = np.polyfit(x=us_df['AverageMarriageAge'], y=us_df['CrudeMarriageRate'], deg=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b4256e",
   "metadata": {},
   "source": [
    "The crude marriage rate in the U.S. decreases as the average marriage age in the United States increases. This hints that there are a lower proportion of older people getting married (at least when age passes 24.0). The confidence interval constantly increases as the average marriage age passes 25.0. This suggests that there is a larger margin of error as the age increases from a certain point, suggesting a larger variation among the crude marriage rates for those older than 25 years old."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "82ceef69",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TotalNetWorth', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=us_df['TotalNetWorth'], y=us_df['CrudeMarriageRate'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57ee194",
   "metadata": {},
   "source": [
    "The regression line shows the crude marriage rate decreasing as the average total networth increases in the United States, suggesting that a greater proportion of relatively richer people get married. Similar to the regression line of Average Marriage Age VS Crude Marriage Rate, the confidence interval increases when x reaches a certain level. In this case, the confidence interval constantly increases after the total net worth (in millions dollar) reaches around 1.5. This suggests that there is a larger margin of error for crude marriage rates after their net worth passes around $1.5mm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d4233a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='BirthsOutsideOfMarriagePercentage', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=us_df['BirthsOutsideOfMarriagePercentage'], y=us_df['CrudeMarriageRate'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a823dec8",
   "metadata": {},
   "source": [
    "The regression line displays a trend where as the birth outside of marriage percentage increases, the crude marriage rate decreases in the United States. The results suggest how there is a lower proportion of those who have children outside of the marriage getting married compared to those who do not (at least for when the birth outside of marriage percentage surpasses 28 to around 40). Unlike the two regression lines above, this one displays a smaller confidence interval as the birth outside of marriage percentage increases. This shows that there is a lower margin of error as the birth outside of marriage percentage increases (less variation in crude marriage rate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2678a5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CohabitationPercentage', ylabel='CrudeMarriageRate'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=us_df['CohabitationPercentage'], y=us_df['CrudeMarriageRate'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d1df73",
   "metadata": {},
   "source": [
    "As the cohabitation percentage increases, the regression line displays the crude marriage rate decreasing. This exhibits how there is a greater proportion people who get married with a greater cohabitation rate, at least for the cohabitation percentage range between 3% to around 7. There is no clear trend in the size of the confidence interval, so the margin of error is arguably the same throughout the fluctuations in cohabitation percentages. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83fe5ea",
   "metadata": {},
   "source": [
    "### Conducting t-test: Crude Marriage Rate VS Average Marriage Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "90b0922d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81db2609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      CrudeMarriageRate   R-squared:                       0.891\n",
      "Model:                            OLS   Adj. R-squared:                  0.886\n",
      "Method:                 Least Squares   F-statistic:                     187.1\n",
      "Date:                Fri, 09 Dec 2022   Prob (F-statistic):           1.55e-12\n",
      "Time:                        23:00:00   Log-Likelihood:                -6.3887\n",
      "No. Observations:                  25   AIC:                             16.78\n",
      "Df Residuals:                      23   BIC:                             19.22\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         36.5286      2.085     17.517      0.000      32.215      40.842\n",
      "x1            -1.1264      0.082    -13.679      0.000      -1.297      -0.956\n",
      "==============================================================================\n",
      "Omnibus:                        0.986   Durbin-Watson:                   0.625\n",
      "Prob(Omnibus):                  0.611   Jarque-Bera (JB):                0.629\n",
      "Skew:                           0.383   Prob(JB):                        0.730\n",
      "Kurtosis:                       2.867   Cond. No.                         812.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "avgage = us_df['AverageMarriageAge']\n",
    "avgage = np.array(avgage)\n",
    "avgage = avgage.reshape(-1,1)\n",
    "marrate = us_df['CrudeMarriageRate']\n",
    "\n",
    "avgage = sm.add_constant(avgage)\n",
    "model = sm.OLS(marrate, avgage)\n",
    "results1 = model.fit()\n",
    "print(results1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ac3b09",
   "metadata": {},
   "source": [
    "The r^2 value is approximately 0.891 for crude marriage rate VS average marriage age, signifying that around 89.1% of the variation in the crude marriage rate is explained by the regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11b2871",
   "metadata": {},
   "source": [
    "### Conducting t-test: Crude Marriage Rate VS Births Outside Of Marriage Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ec779861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      CrudeMarriageRate   R-squared:                       0.947\n",
      "Model:                            OLS   Adj. R-squared:                  0.945\n",
      "Method:                 Least Squares   F-statistic:                     412.6\n",
      "Date:                Fri, 09 Dec 2022   Prob (F-statistic):           3.45e-16\n",
      "Time:                        23:00:00   Log-Likelihood:                 2.7240\n",
      "No. Observations:                  25   AIC:                            -1.448\n",
      "Df Residuals:                      23   BIC:                            0.9897\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         15.9424      0.393     40.583      0.000      15.130      16.755\n",
      "x1            -0.2240      0.011    -20.312      0.000      -0.247      -0.201\n",
      "==============================================================================\n",
      "Omnibus:                        3.318   Durbin-Watson:                   0.814\n",
      "Prob(Omnibus):                  0.190   Jarque-Bera (JB):                1.770\n",
      "Skew:                          -0.575   Prob(JB):                        0.413\n",
      "Kurtosis:                       3.614   Cond. No.                         310.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "birth = us_df['BirthsOutsideOfMarriagePercentage']\n",
    "birth = np.array(birth)\n",
    "birth = birth.reshape(-1,1)\n",
    "\n",
    "birth = sm.add_constant(birth)\n",
    "model = sm.OLS(marrate, birth)\n",
    "results2 = model.fit()\n",
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f08efe",
   "metadata": {},
   "source": [
    "The r^2 value is approximately 0.947 for crude marriage rate VS births outside of marriage percentage, signifying that around 94.7% of the variation in the crude marriage rate is explained by the regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7443b635",
   "metadata": {},
   "source": [
    "### Conducting t-test: Crude Marriage Rate VS Cohabitation Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e0fe5833",
   "metadata": {},
   "outputs": [],
   "source": [
    "cohab = us_df['CohabitationPercentage']\n",
    "cohab = np.array(cohab)\n",
    "cohab = cohab.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b7df3747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      CrudeMarriageRate   R-squared:                       0.838\n",
      "Model:                            OLS   Adj. R-squared:                  0.831\n",
      "Method:                 Least Squares   F-statistic:                     119.1\n",
      "Date:                Fri, 09 Dec 2022   Prob (F-statistic):           1.43e-10\n",
      "Time:                        23:00:00   Log-Likelihood:                -11.275\n",
      "No. Observations:                  25   AIC:                             26.55\n",
      "Df Residuals:                      23   BIC:                             28.99\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         10.7217      0.260     41.200      0.000      10.183      11.260\n",
      "x1            -0.5613      0.051    -10.915      0.000      -0.668      -0.455\n",
      "==============================================================================\n",
      "Omnibus:                        1.601   Durbin-Watson:                   0.541\n",
      "Prob(Omnibus):                  0.449   Jarque-Bera (JB):                1.133\n",
      "Skew:                           0.254   Prob(JB):                        0.567\n",
      "Kurtosis:                       2.089   Cond. No.                         17.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(cohab, marrate)\n",
    "cohab = sm.add_constant(cohab)\n",
    "model = sm.OLS(marrate, cohab)\n",
    "results3 = model.fit()\n",
    "print(results3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11543cfb",
   "metadata": {},
   "source": [
    "The r^2 value is approximately 0.838 for crude marriage rate VS cohabitation percentage, signifying that around 83.8% of the variation in the crude marriage rate is explained by the regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70645fcc",
   "metadata": {},
   "source": [
    "### Results from t-tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb0a0c5",
   "metadata": {},
   "source": [
    "The p-value is 0.00 for all the dependent variables which is less than our significance level of 0.05. Therefore, there is a significant linear relationship between average marriage age and the crude marriage rate, births outside of marriage percentage and crude marriage rate and cohabitation percentage and the crude marriage rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd02fd1",
   "metadata": {},
   "source": [
    "## Exponential Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196e280e",
   "metadata": {},
   "source": [
    "To further forecast our data, we are utilizing Simple Exponential Smoothing method where forecast for time t+1 is a weighted average of the new observation (time t) and the previous forecast (for time t). We decided to show two forecasts using two levels of alpha, the smoothing constant. However, as we are focusing more on accuracy than smoothing the data points, we would prioritize higher alpha (0.6) than the lower one (0.2)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30900d7",
   "metadata": {},
   "source": [
    "### Crude Marriage Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f75a31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fc7d37a60>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "crude = us_df[['CrudeMarriageRate']]\n",
    "crude1 = crude.values.reshape(-1).tolist()\n",
    "\n",
    "data = crude1\n",
    "index = pd.date_range(start = \"1990\", end=\"2015\", freq = \"A\")\n",
    "ndata = pd.Series(data,index)\n",
    "\n",
    "fit1 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.2, optimized=False\n",
    ")\n",
    "fcast1 = fit1.forecast(3).rename(r\"$\\alpha=0.2$\")\n",
    "fit2 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.6, optimized=False\n",
    ")\n",
    "fcast2 = fit2.forecast(3).rename(r\"$\\alpha=0.6$\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(ndata, marker=\"o\", color=\"black\")\n",
    "plt.plot(fit1.fittedvalues, marker=\"o\", color=\"blue\")\n",
    "(line1,) = plt.plot(fcast1, marker=\"o\", color=\"blue\")\n",
    "plt.plot(fit2.fittedvalues, marker=\"o\", color=\"red\")\n",
    "(line2,) = plt.plot(fcast2, marker=\"o\", color=\"red\")\n",
    "plt.legend([line1,line2], [fcast1.name, fcast2.name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b687f622",
   "metadata": {},
   "source": [
    "Looking at the graph above, focusing on smoothing constant of 0.6, we can expect the crude marriage rate in United States to slighlty decline ranging from 6.8 to 6.9 in 2015 as it has been for past 3 years. Comparing to the actual outcome that we have gathered from our original data, the crude rate was recorded to be 6.9 in 2015, which is very similar to our forecast using the Simple Exponential Smoothing (alpha = 0.6)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f640b7",
   "metadata": {},
   "source": [
    "### Average Marriage Age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "59492873",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1990-12-31', '1991-12-31', '1992-12-31', '1993-12-31',\n",
      "               '1994-12-31', '1995-12-31', '1996-12-31', '1997-12-31',\n",
      "               '1998-12-31', '1999-12-31', '2000-12-31', '2001-12-31',\n",
      "               '2002-12-31', '2003-12-31', '2004-12-31', '2005-12-31',\n",
      "               '2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n",
      "               '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
      "               '2014-12-31'],\n",
      "              dtype='datetime64[ns]', freq='A-DEC')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fc84f0b80>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = us_df[['AverageMarriageAge']]\n",
    "age1 = age.values.reshape(-1).tolist()\n",
    "\n",
    "data = age1\n",
    "index = pd.date_range(start = \"1990\", end=\"2015\", freq = \"A\")\n",
    "ndata = pd.Series(data,index)\n",
    "print(index)\n",
    "\n",
    "fit1 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.2, optimized=False\n",
    ")\n",
    "fcast1 = fit1.forecast(3).rename(r\"$\\alpha=0.2$\")\n",
    "fit2 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.6, optimized=False\n",
    ")\n",
    "fcast2 = fit2.forecast(3).rename(r\"$\\alpha=0.6$\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(ndata, marker=\"o\", color=\"black\")\n",
    "plt.plot(fit1.fittedvalues, marker=\"o\", color=\"blue\")\n",
    "(line1,) = plt.plot(fcast1, marker=\"o\", color=\"blue\")\n",
    "plt.plot(fit2.fittedvalues, marker=\"o\", color=\"red\")\n",
    "(line2,) = plt.plot(fcast2, marker=\"o\", color=\"red\")\n",
    "plt.legend([line1,line2], [fcast1.name, fcast2.name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fd5d01",
   "metadata": {},
   "source": [
    "Looking at the graph above, focusing on smoothing constant of 0.6, we can expect the avereage marriage age in United States to moderately increase to near 26.5. On the other hand, the most recent 2014 data reached 27, which was a ubrupt increase. Comparing to the actual outcome that we have gathered from our original data, the average marriage rate was recorded to be 27.1 in 2015, which is significantly higher than our forecast using the Simple Exponential Smoothing (alpha = 0.6). Thus, our forecast using the Simple Exponential Smoothing seems a bit lower than what it should be regarding the trend we witness where we suggest another trend model to better forecast the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e317d8",
   "metadata": {},
   "source": [
    "### Total Net Worth Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8fc027fd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1990-12-31', '1991-12-31', '1992-12-31', '1993-12-31',\n",
      "               '1994-12-31', '1995-12-31', '1996-12-31', '1997-12-31',\n",
      "               '1998-12-31', '1999-12-31', '2000-12-31', '2001-12-31',\n",
      "               '2002-12-31', '2003-12-31', '2004-12-31', '2005-12-31',\n",
      "               '2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n",
      "               '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
      "               '2014-12-31'],\n",
      "              dtype='datetime64[ns]', freq='A-DEC')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fc87df250>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "networth = us_df[['TotalNetWorth']]\n",
    "networth1 = networth.values.reshape(-1).tolist()\n",
    "\n",
    "data = networth1\n",
    "index = pd.date_range(start = \"1990\", end=\"2015\", freq = \"A\")\n",
    "ndata = pd.Series(data,index)\n",
    "print(index)\n",
    "\n",
    "fit1 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.2, optimized=False\n",
    ")\n",
    "fcast1 = fit1.forecast(3).rename(r\"$\\alpha=0.2$\")\n",
    "fit2 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.6, optimized=False\n",
    ")\n",
    "fcast2 = fit2.forecast(3).rename(r\"$\\alpha=0.6$\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(ndata, marker=\"o\", color=\"black\")\n",
    "plt.plot(fit1.fittedvalues, marker=\"o\", color=\"blue\")\n",
    "(line1,) = plt.plot(fcast1, marker=\"o\", color=\"blue\")\n",
    "plt.plot(fit2.fittedvalues, marker=\"o\", color=\"red\")\n",
    "(line2,) = plt.plot(fcast2, marker=\"o\", color=\"red\")\n",
    "plt.legend([line1,line2], [fcast1.name, fcast2.name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faaf3ebe",
   "metadata": {},
   "source": [
    "Looking at the graph above, focusing on smoothing constant of 0.6, we can expect the total net worth percentage in United States to significantly increase to near 2.75 billion. On the other hand, the most recent 2014 data reached above 3.18 billion, which was an ubrupt increase and much higher than our forecast. As our forecast for 2015 is lower than the actual data for 2014 and 2015, the Simple Exponential Smoothing method might not be the perfect forecasting model to use for total net worth percentage where we suggest using other trend model for future uses. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfc3113",
   "metadata": {},
   "source": [
    "### Births Outside of Marriage Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "da7d749c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1990-12-31', '1991-12-31', '1992-12-31', '1993-12-31',\n",
      "               '1994-12-31', '1995-12-31', '1996-12-31', '1997-12-31',\n",
      "               '1998-12-31', '1999-12-31', '2000-12-31', '2001-12-31',\n",
      "               '2002-12-31', '2003-12-31', '2004-12-31', '2005-12-31',\n",
      "               '2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n",
      "               '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
      "               '2014-12-31'],\n",
      "              dtype='datetime64[ns]', freq='A-DEC')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fc8eef850>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "birth = us_df[['BirthsOutsideOfMarriagePercentage']]\n",
    "birth1 = birth.values.reshape(-1).tolist()\n",
    "\n",
    "data = birth1\n",
    "index = pd.date_range(start = \"1990\", end=\"2015\", freq = \"A\")\n",
    "ndata = pd.Series(data,index)\n",
    "print(index)\n",
    "\n",
    "fit1 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.2, optimized=False\n",
    ")\n",
    "fcast1 = fit1.forecast(3).rename(r\"$\\alpha=0.2$\")\n",
    "fit2 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.6, optimized=False\n",
    ")\n",
    "fcast2 = fit2.forecast(3).rename(r\"$\\alpha=0.6$\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(ndata, marker=\"o\", color=\"black\")\n",
    "plt.plot(fit1.fittedvalues, marker=\"o\", color=\"blue\")\n",
    "(line1,) = plt.plot(fcast1, marker=\"o\", color=\"blue\")\n",
    "plt.plot(fit2.fittedvalues, marker=\"o\", color=\"red\")\n",
    "(line2,) = plt.plot(fcast2, marker=\"o\", color=\"red\")\n",
    "plt.legend([line1,line2], [fcast1.name, fcast2.name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a211b7b",
   "metadata": {},
   "source": [
    "Looking at the graph above, focusing on smoothing constant of 0.6, we can expect the births outside of marriage percentage in United States to slightly decrease to near 40.6. Our model using smoothing constant of 0.6 seems to reasonably track and smooth the data, taking the increasing trend into account. Thus, we believe our Simple Exponential Smoothing model above should accurately forecast births outside of marriage percentage in 2015 to around 40.6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af7c763",
   "metadata": {},
   "source": [
    "### Cohabitation Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4e1b9ffc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1990-12-31', '1991-12-31', '1992-12-31', '1993-12-31',\n",
      "               '1994-12-31', '1995-12-31', '1996-12-31', '1997-12-31',\n",
      "               '1998-12-31', '1999-12-31', '2000-12-31', '2001-12-31',\n",
      "               '2002-12-31', '2003-12-31', '2004-12-31', '2005-12-31',\n",
      "               '2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n",
      "               '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
      "               '2014-12-31'],\n",
      "              dtype='datetime64[ns]', freq='A-DEC')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fa96027f0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohab = us_df[['CohabitationPercentage']]\n",
    "cohab1 = cohab.values.reshape(-1).tolist()\n",
    "\n",
    "data = cohab1\n",
    "index = pd.date_range(start = \"1990\", end=\"2015\", freq = \"A\")\n",
    "ndata = pd.Series(data,index)\n",
    "print(index)\n",
    "\n",
    "fit1 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.2, optimized=False\n",
    ")\n",
    "fcast1 = fit1.forecast(3).rename(r\"$\\alpha=0.2$\")\n",
    "fit2 = SimpleExpSmoothing(ndata, initialization_method=\"heuristic\").fit(\n",
    "    smoothing_level=0.6, optimized=False\n",
    ")\n",
    "fcast2 = fit2.forecast(3).rename(r\"$\\alpha=0.6$\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(ndata, marker=\"o\", color=\"black\")\n",
    "plt.plot(fit1.fittedvalues, marker=\"o\", color=\"blue\")\n",
    "(line1,) = plt.plot(fcast1, marker=\"o\", color=\"blue\")\n",
    "plt.plot(fit2.fittedvalues, marker=\"o\", color=\"red\")\n",
    "(line2,) = plt.plot(fcast2, marker=\"o\", color=\"red\")\n",
    "plt.legend([line1,line2], [fcast1.name, fcast2.name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b40f0fd",
   "metadata": {},
   "source": [
    "Looking at the graph above, focusing on smoothing constant of 0.6, we can expect the cohabitation percentage in United States to moderately increase to near 7.3 and 7.4. Our model using smoothing constant of 0.6 seems to reasonably track and smooth the data, taking the increasing trend into account. Thus, we believe our Simple Exponential Smoothing model above should accurately forecast cohabitation percentage in 2015 to around 7.3~7.4."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dba6eb7",
   "metadata": {},
   "source": [
    "## Conclusion \n",
    "\n",
    "Our project was to explore how age and wealth affect marriage rates in UK, Italy, Germany, Austria, Switzerland, Japan, Mexico, Canada, United States, and Australia. These countries were picked to compare on aspects of population and proximity to see whether there could be a correlation or a similar trend amongst them. We also explored how cohabitation and birth outside of marriage affect the marriage rates in the United States. \n",
    "\n",
    "We had a few hypotheses including: \n",
    "- H1: Countries within greater proximities will exhibit higher correlations between average marriage age and marriage rates.\n",
    "- H2: Countries with similar population sizes will exhibit higher correlations between average marriage and marriage rates, although not as high compared to countries within greater proximities. \n",
    "- H3: We predict that wealth would be the factor that shows the strongest correlation with marriage rates in the United States. \n",
    "- H4: We predict that each country would display the same/similar relationship between average marriage age and marriage rates. \n",
    "- H5: However, we predict that there won't be a significant correlation or similarity based on population size.\n",
    "- H6: We believe that there wouldn't be a correlation between population size and marriage.\n",
    "- H7: We also believe that there could be a correlation between the factors. We believe that there could be a strong correlation between cohabitation and birth outside of marriage and a strong correlation betwen age and wealth.\n",
    "\n",
    "We cleaned our data, found summary statistics, and plotted the marriage rates and average marriage age of all the countries mentioned to see the overall trend, where we specifically described the trends above. Overall, we could see a decreasing trend in crude marriage globally throughout the years while the global average marriage age has increased over the years. This supported our hypothesis 4, predicting that each country would display the same/similar relationship between average marriage age and marriage rates.  \n",
    "\n",
    "In terms of H1: Countries within greater proximities will exhibit higher correlations between average marriage age and marriage rates, this was supported in our results, where we found that countries within greater proximities like UK, Italy and Germany exhibited higher correlations between average marriage age and crude marriage rates. \n",
    "\n",
    "For H2: countries with similar population sizes will exhibit higher correlations between average marriage and marriage rates, although not as high compared to countries within greater proximities, H2 was also supported which was interesting to see that in the comparison between Germany, Italy, and UK, Germany had a significantly lower correlation in relative to the other two (Germany: -0.75, UK: -0.97, Italy: -0.98) which may suggest that population size could have a large effect on correlation between the two variables. Similarly, for Japan and Mexico (which was mainly used to compare the population size) shows that there are high correlations but not as high compared to countries within greater proximities. In addition, H2 was rather not evident in the comparison between Austria and Switzerland, where they are compared for both proximity and population size, where the difference between the correlation of the 2 variables (age and marriage) are not as similar as we expected. \n",
    "\n",
    "In terms of H3: we predict that wealth would be the factor that shows the strongest correlation with marriage rates in the United States, through our correlation matrix, we could see that wealth wasn’t the factor that shows the strongest correlation with marriage rates in the US (-0.95) but rather births outside of marriage percentage (-0.97). \n",
    "\n",
    "For H5 and H6, it was challenging to conclude any relevant data that could support/reject our hypothesis so if we had more time, I think we should have explored deeper into this aspect. \n",
    "\n",
    "Finally, for H7, H7 was supported in terms of how there are correlations between the factors and there was a strong correlation between cohabitation and birth outside of marriage of 0.94 but there was a stronger correlation between age and wealth. \n",
    "\n",
    "Running the t-tests on linear regression using OSL summary, the summary displays high r^2 values for relationships between the average marriage age and the crude marriage rate, births outside of marriage percentage and crude marriage rate and cohabitation percentage and the crude marriage rate. This shows how the regression lines is a good fit for the relationship between these variables. In addition, the OLS generated p-values that are all around 0.000, allowing us to conclude that there is a significant linear relationship between the variables at the significance level of 5%; we are able to reject the null hypotheses that there are no significant linear relationships between the variables. \n",
    "\n",
    "In conclusion, we were able to find data that supported most of our hypotheses and realized very interesting insights about the correlation between marriage rates and other factors including age, wealth, births outside of marriage, and cohabitation. "
   ]
  }
 ],
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