#' SAT and GPA data
#'
#' SAT and GPA data for 1000 students at an unnamed college.
#'
#'
#' @name satgpa
#' @docType data
#' @format A data frame with 1000 observations on the following 6 variables.
#' \describe{
#' \item{sex}{Gender of the student.}
#' \item{sat_v}{Verbal SAT percentile.}
#' \item{sat_m}{Math SAT percentile.}
#' \item{sat_sum}{Total of verbal and math SAT percentiles.}
#' \item{hs_gpa}{High school grade point average.}
#' \item{fy_gpa}{First year (college) grade point average.}
#' }
#' @references https://chance.dartmouth.edu/course/Syllabi/Princeton96/ETSValidation.html
#' @source Educational Testing Service originally collected the data.
#' @keywords datasets
#' @examples
#'
#' library(ggplot2)
#' library(broom)
#'
#' # Verbal scores
#' ggplot(satgpa, aes(x = sat_v, fy_gpa)) +
#' geom_point() +
#' geom_smooth(method = "lm") +
#' labs(
#' x = "Verbal SAT percentile",
#' y = "First year (college) grade point average"
#' )
#'
#' mod <- lm(fy_gpa ~ sat_v, data = satgpa)
#' tidy(mod)
#'
#' # Math scores
#' ggplot(satgpa, aes(x = sat_m, fy_gpa)) +
#' geom_point() +
#' geom_smooth(method = "lm") +
#' labs(
#' x = "Math SAT percentile",
#' y = "First year (college) grade point average"
#' )
#'
#' mod <- lm(fy_gpa ~ sat_m, data = satgpa)
#' tidy(mod)
"satgpa"
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