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#' Simulated data bearing on school vouchers
#'
#' In the US, there have been controversial proposals to provide vouchers
#' to students in failing public schools. The vouchers would allow the students to attend
#' private schools. There are arguments pro and con that are often rooted in political
#' philosophy (free choice!) and politics. The presumption behind the *pro* arguments
#' is that attending private schools would create better outcomes for students.
#'
#' A reasonable way to test this presumption is to compare test scores for students
#' in public and private schools.
#' One famous analysis (Howell and Peterson, 2003, "The Education Gap: Vouchers and Urban Schools")
#' found that voucher schools are most helpful for African-American students,
#' and not so much for white or Hispanic students.
#'
#' The \code{School_data} data frame comes from a simulation designed by the package author to
#' replicate the overall results but supporting a very different policy recommendation. WARNING: This is
#' just a simulation, reflecting one hypothesis about how the world might work. Don't be tempted
#' to draw conclusions about the actual factors involved in school performance from such simulated data.
#'
#'
#' @docType data
#' @name School_data
#' @usage data(School_data)
#'
#' @keywords datasets
#'
#' @format
#' A data frame with 500 cases, each of which is a simulated student, with observations on the following variables.
#' \itemize{
#' \item{\code{test_score}} {a simulated test score for the student}
#' \item{\code{school}} {whether the student attended public or private school}
#' \item{\code{lottery}} {whether the student was entered into a lottery for a private-school voucher}
#' \item{\code{group}} {the racial/ethnic group of the student}
#' \item{\code{acad_motivation}} {the overall level of involvement and concern of the student's parents for the student's academic performance}
#' \item{\code{relig_motivation}} {the overall level of interest motivated by religion. This is
#' potentially an issue because a large majority of urban private schools are Catholic.}
#' }
#'
#'
#' @examples
#' lm(test_score ~ school, data = School_data)
#' # the simulation mechanism itself:
#' nstudents <- 500
#' acad_motivation <- rnorm(nstudents)
#' group <- sample(c("black", "hispanic", "white"), replace = TRUE, size = nstudents)
#' relig_motivation <- ifelse( group == "black", -1, ifelse(group == "white", 0, 1))
#' relig_motivation <- rnorm(nstudents, mean = relig_motivation)
#' lottery <- (acad_motivation + relig_motivation) > 0
#' school <- ifelse( (runif(nstudents) + .8* lottery ) > 1, "private", "public")
#' test_score <- rnorm(nstudents, mean = 100 - 5 * (school == "private") + 20 * acad_motivation)
#' School_data <- data.frame(test_score, acad_motivation, group, relig_motivation, lottery, school)
"School_data"
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