diff --git a/NAMESPACE b/NAMESPACE
index c4768d8..468baf3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -33,6 +33,7 @@ export(theme_coursekata)
 export(upper)
 import(rlang)
 importFrom(Lock5withR,locket)
+importFrom(Metrics,sse)
 importFrom(dslabs,take_poll)
 importFrom(glue,glue)
 importFrom(lifecycle,deprecated)
diff --git a/R/coursekata-package.R b/R/coursekata-package.R
index 03058e9..45eeafe 100644
--- a/R/coursekata-package.R
+++ b/R/coursekata-package.R
@@ -14,4 +14,5 @@ NULL
 #' @importFrom lsr cohensD
 #' @importFrom mosaic qdist
 #' @importFrom supernova supernova
+#' @importFrom Metrics sse
 NULL
diff --git a/R/zargle.R b/R/zargle.R
deleted file mode 100644
index 2cce052..0000000
--- a/R/zargle.R
+++ /dev/null
@@ -1,44 +0,0 @@
-#' Kargle, Bargle, and Zargle video game score simulator.
-#'
-#' Kargle, Bargle, and Zargle are three fictitious video games referenced in the
-#' CourseKata Statistics and Data Science course. The games are used to introduce
-#' the concept of the standard deviation. This function simulates scores from the
-#' three games and allows students to explore how differences in sample size, mean,
-#' and standard deviation affect the distribution of scores.
-compare_score_distributions <- function(sd = 3500, mean = 35000, n = 1000, ..., .seed = 5) {
-  set.seed(.seed)
-  kargle <- simulate_scores("Kargle", 1000, 35000, 5000)
-  bargle <- simulate_scores("Bargle", 1000, 35000, 1000)
-  zargle <- simulate_scores("Zargle", n, mean, sd)
-  games <- vctrs::vec_c(kargle, bargle, zargle)
-
-  # combine all zones > 3 into a single "outside 3" zone
-  games$zone <- ifelse(games$zone > 3, "outside 3", games$zone)
-  # convert the proportions to cumulative proportions for all except "outside 3"
-  props <- data.frame(tally(zone ~ game, data = games, format = "proportion"))
-  props <- purrr::map_dfr(split(props, props$game), function(x) {
-    x$Freq <- c(cumsum(x$Freq[1:3]), x$Freq[4])
-    x
-  })
-  # re-format the table to be wide (one column per game)
-  zone_table <- tidyr::pivot_wider(props, names_from = game, values_from = Freq)
-
-  print(data.frame(zone_table))
-  gf_histogram(~scores, fill = ~zone, data = games, bins = 160, alpha = .8) %>%
-    gf_facet_grid(game ~ .)
-}
-
-#' Simulate score for one of the *argle games.
-#'
-#' @param game The name of the game.
-#' @param n The number of scores to simulate.
-#' @param mean The mean of the scores.
-#' @param sd The standard deviation of the scores.
-#' @return A data frame with the simulated scores.
-#' @keywords internal
-simulate_scores <- function(game, n, mean, sd) {
-  scores <- rnorm(n, mean, sd)
-  z <- (scores - mean) / sd
-  interval <- ifelse(z > 0, trunc(1 + z), trunc(z - 1))
-  data.frame(game = game, scores = scores, z = z, interval = interval, zone = abs(interval))
-}
diff --git a/man/compare_score_distributions.Rd b/man/compare_score_distributions.Rd
deleted file mode 100644
index 64280e1..0000000
--- a/man/compare_score_distributions.Rd
+++ /dev/null
@@ -1,15 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/zargle.R
-\name{compare_score_distributions}
-\alias{compare_score_distributions}
-\title{Kargle, Bargle, and Zargle video game score simulator.}
-\usage{
-compare_score_distributions(sd = 3500, mean = 35000, n = 1000, ..., .seed = 5)
-}
-\description{
-Kargle, Bargle, and Zargle are three fictitious video games referenced in the
-CourseKata Statistics and Data Science course. The games are used to introduce
-the concept of the standard deviation. This function simulates scores from the
-three games and allows students to explore how differences in sample size, mean,
-and standard deviation affect the distribution of scores.
-}
diff --git a/man/simulate_scores.Rd b/man/simulate_scores.Rd
deleted file mode 100644
index 8d6c6fd..0000000
--- a/man/simulate_scores.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/zargle.R
-\name{simulate_scores}
-\alias{simulate_scores}
-\title{Simulate score for one of the *argle games.}
-\usage{
-simulate_scores(game, n, mean, sd)
-}
-\arguments{
-\item{game}{The name of the game.}
-
-\item{n}{The number of scores to simulate.}
-
-\item{mean}{The mean of the scores.}
-
-\item{sd}{The standard deviation of the scores.}
-}
-\value{
-A data frame with the simulated scores.
-}
-\description{
-Simulate score for one of the *argle games.
-}
-\keyword{internal}