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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chem_proc_yield.R
\docType{data}
\name{chem_proc_yield}
\alias{chem_proc_yield}
\title{Chemical manufacturing process data set}
\source{
Kuhn, Max, and Kjell Johnson. \emph{Applied predictive modeling}. New York:
Springer, 2013.
}
\value{
\item{chem_proc_yield}{a tibble}
}
\description{
A data set that models yield as a function of biological material predictors
and chemical structure predictors.
}
\details{
This data set contains information about a chemical manufacturing
process, in which the goal is to understand the relationship between
the process and the resulting final product yield. Raw material in
this process is put through a sequence of 27 steps to generate the
final pharmaceutical product. The starting material is generated from
a biological unit and has a range of quality and characteristics. The
objective in this project was to develop a model to predict percent
yield of the manufacturing process. The data set consisted of 177
samples of biological material for which 57 characteristics were
measured. Of the 57 characteristics, there were 12 measurements of
the biological starting material, and 45 measurements of the
manufacturing process. The process variables included measurements
such as temperature, drying time, washing time, and concentrations of
by-products at various steps. Some of the process measurements can
be controlled, while others are observed. Predictors are continuous,
count, categorical; some are correlated, and some contain missing
values. Samples are not independent because sets of samples come from
the same batch of biological starting material.
Columns:
\itemize{
\item \code{yield}: numeric
\item \code{bio_material_01} - \code{bio_material_12}: numeric
\item \code{man_proc_01} - \code{man_proc_45}: numeric
}
}
\examples{
data(chem_proc_yield)
str(chem_proc_yield)
}
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