qacData: Datasets for Data Science Instruction

#' Housing Values in Suburbs of Boston
#'
#' Housing Values in Suburbs of Boston
#'
#' @format A data frame with 506 rows and 14 variables:
#' \describe{
#'   \item{\code{crim}}{per capita crime rate by town.}
#'   \item{\code{zn}}{proportion of residential land zoned for lots over 25,000 sq.ft.}
#'   \item{\code{indus}}{proportion of non-retail business acres per town.}
#'   \item{\code{chas}}{Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).}
#'   \item{\code{nox}}{nitrogen oxides concentration (parts per 10 million).}
#'   \item{\code{rm}}{average number of rooms per dwelling.}
#'   \item{\code{age}}{proportion of owner-occupied units built prior to 1940.}
#'   \item{\code{dis}}{weighted mean of distances to five Boston employment centres.}
#'   \item{\code{rad}}{index of accessibility to radial highways.}
#'   \item{\code{tax}}{full-value property-tax rate per $10,000.}
#'   \item{\code{ptratio}}{pupil-teacher ratio by town.}
#'   \item{\code{black}}{proportion of blacks by town.}
#'   \item{\code{lstat}}{lower status of the population (percent).}
#'   \item{\code{medv}}{median value of owner-occupied homes in $1000s.}
#' }
#' @details 
#' The \code{\link[MASS]{Boston}} data frame was obtained from Venables and Ripley's \code{MASS} package.
#' The data utilize census tracts in the Boston Standard Metropolitatn 
#' Statistical Area in 1970. Two changes have been made from this original
#' dataset. The dollar values for \code{tax} and 
#' \code{medv} have been converted to 2020 US dollars (assuming a 299.4\% 
#' cumulative inflation rate). Additionally, the \code{black} variable has 
#' been transformed from its original metric
#' (1000*(proportion of blacks by town - 0.63)^2) to a simple the proportion 
#' of blacks by town.
#' 
#' @source 
#' Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.
#' 
#' Belsley D.A., Kuh, E. and Welsch, R.E. (1980) 
#' Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
#' @examples 
#' summary(Boston)
"Boston"