% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentation.R
\docType{data}
\name{chapter_9_table_12}
\alias{chapter_9_table_12}
\alias{C9T12}
\alias{Chapter_9_Table_12}
\alias{c9t12}
\title{The data used in Chapter 9, Table 12}
\format{
An object of class \code{data.frame} with 18 rows and 4 columns.
}
\source{
\url{https://designingexperiments.com/data/}

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). \emph{Designing experiments and
analyzing data: {A} model comparison perspective}. (4th ed.). New York, NY: Routledge.
}
\usage{
data(chapter_9_table_12)
}
\description{
The data used in Chapter 9, Table 12
}
\details{
The question of interest in the present situation assumes that there are three blocks of elderly participants, six per block. The elderly participants are sorted into the three blocks as a function of their age. The purpose of the study was to assess the effect of age on motor control, measured by the number of errors on a certain task (where there were three tasks). The goal here is to replicate the results from the top portion of Table 9.12 (At this point performing the ANOVA and the ANCOVA also included in Table 9.12 should be straightforward.
}
\note{
Renumbered for the 4th edition: in the 3rd edition (AMCP 1.x) these data were Table 9.11 (\code{chapter_9_table_11} / \code{C9T11}). The data are unchanged.
}
\section{Variables}{

\describe{
\item{Block}{a numeric vector between 1 and 3, equal to the block of elderly participants (6 per block)}
\item{Task}{the task given}
\item{X}{age}
\item{Y}{error scores}
}
}

\section{Synonym}{

C9T12
}

\examples{
# Load the data
data(chapter_9_table_12)

# Or, alternatively load the data as
data(C9T12)

# View the structure
str(chapter_9_table_12)

# ---------------------------------------------------------------------
# Optional: a factor-coded copy for model-comparison analyses / plotting.
# Block and Task are stored as numeric codes so the book's examples
# reproduce exactly. The "Variables" section does not give level labels,
# so the numeric codes are kept as the factor levels. Build a *copy*
# (suffix "_factors") so the canonical data set is left unchanged. The
# covariate X (age) and the outcome Y stay numeric.
C9T12_factors <- chapter_9_table_12
C9T12_factors$Block <- factor(C9T12_factors$Block)
C9T12_factors$Task  <- factor(C9T12_factors$Task)

# The book analyzes these data with ANOVA and ANCOVA (Table 9.12); build
# the factor copy, then follow the book's procedure (age, X, is the
# covariate, kept numeric).
str(C9T12_factors)

}
\references{
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). \emph{Designing experiments and analyzing data:
{A} model comparison perspective} (4th ed.). New York, NY: Routledge.
}
\author{
Ken Kelley \email{kkelley@nd.edu}
}
\keyword{datasets}
