stat340: Data and functions for BIAS at NMBU

#' DISCRIM: Gender Discrimination in Hiring
#'
#' This data contains information on the amount of education and work
#' experience for 28 former male and female applicants. An equal-right group
#' claims that females are less likely to be hired than males with the same
#' background.
#'
#' @format A data frame with 28 observations (rows) and 4 variables (columns).
#'
#' \tabular{llllr}{
#'   \tab \bold{Column name} \tab \bold{Data type} \tab \bold{Description} \tab \bold{Values}\cr
#'   \code{[,1]} \tab \code{HIRE} \tab numeric \tab 1 if the person was hired, otherwise 0 \tab (0 - 1)\cr
#'   \code{[,2]} \tab \code{EDUC} \tab numeric \tab Years of higher education \tab (4 - 8)\cr
#'   \code{[,3]} \tab \code{EXP} \tab numeric \tab Years of experience \tab (0 - 12)\cr
#'   \code{[,4]} \tab \code{GENDER} \tab numeric \tab 1 if male applicant, otherwise 0 \tab (0 - 1)
#' }
#'
#' @details
#'
#' The data is from Exercise 9.21 in A second course in statistics:
#' Regression Analysis, 7th edition.
#'
#' @examples
#'
#' # A short summary of the variables
#' summary(DISCRIM)
#'
#' # Structure of the object
#' str(DISCRIM)
#'
#' @source Mendenhall, W., Sincich, T. (2012) \emph{A second course in statistics:
#' Regression Analysis, 7th edition.}. Pearson Education.
#'
#' @docType data
#' @keywords datasets
#' @name DISCRIM
#' @usage DISCRIM
#'
"DISCRIM"