flipRegression: Estimates standard regression models

#' @name technology.unstacked
#'
#' @title Technology Brand data unstacked
#' @docType data
#' @description Case study from the technology market where 302 respondents rated how likely they were to
#' recommend 13 tech brands. For a series of 9 attributes, the respondents indicated which brands they
#' felt possessed the attribute.
#' @format This list contains two \code{data.frame} elements:
#' \itemize{
#' \item \code{Y}: A \code{data.frame} that contains the recommendation values on a scale of 0 to 10.
#'  This \code{data.frame} has 13 columns, one for each brand (\code{Apple}, \code{Microsoft}, \code{IBM}, \code{Google}, \code{Intel}, \code{Hewlett-Packard}, \code{Sony}, \code{Dell}, \code{Yahoo}, \code{Nokia}, \code{Samsung}, \code{LG}, \code{Panasonic}).
#' \item \code{X}:  A \code{data.frame} that contains the respondents values for each attribute on each
#' brand. There are 117 columns, one of each of the 9 attributes by 13 brands. The nine attributes are (\code{Fun}, \code{Worth what you pay for}, \code{Innovative}, \code{Good customer service}, \code{Stylish}, \code{Easy to use}, \code{High quality}, \code{High performance} and  \code{Low prices}). The names of the columns are of the form 'attribute, brand'.
#' }
#' @source
#'
#' These data are available from the Q research software website. The address is: \url{https://wiki.q-researchsoftware.com/images/3/35/Technology_2018.sav}
#'
#' More details of the analysis and stacking available in the 'How to do Driver Analysis: Ebook' available
#' on the Displayr website. \url{https://www.displayr.com}
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