% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{auto_mpg}
\alias{auto_mpg}
\title{Auto MPG Dataset}
\format{
A data frame with 398 rows and 9 variables:
\describe{
\item{mpg}{Miles per gallon (fuel efficiency)}
\item{cylinders}{Number of cylinders (4, 6, or 8)}
\item{displacement}{Engine displacement (cubic inches)}
\item{horsepower}{Engine horsepower (6 missing values)}
\item{weight}{Vehicle weight (pounds)}
\item{acceleration}{Time to accelerate from 0 to 60 mph (seconds)}
\item{model_year}{Model year (70-82, i.e., 1970-1982)}
\item{origin}{Origin (1 = American, 2 = European, 3 = Japanese)}
\item{car_name}{Car model name}
}
}
\source{
UCI Machine Learning Repository
\url{https://archive.ics.uci.edu/dataset/9/auto+mpg}
}
\usage{
auto_mpg
}
\description{
Fuel consumption and vehicle characteristics for 398 automobiles
from the 1970s and 1980s. This dataset is used in published PMM research
to demonstrate PMM2 on asymmetric regression residuals and to illustrate
dispatcher diagnostics on practical automotive data.
}
\details{
Practical regression examples:
\itemize{
\item \strong{MPG vs Acceleration} (PMM2, linear): residuals have
gamma3 = 0.49, g2 = 0.86 (Zabolotnii et al., 2018)
\item \strong{MPG vs Weight} (PMM2, quadratic): residuals have
gamma3 = 0.8, g2 = 0.83 (Zabolotnii et al., 2025)
\item \strong{MPG vs Horsepower} (diagnostic, quadratic): residuals are
nearly symmetric but have positive excess kurtosis, so the current
\code{pmm_dispatch()} rule does not treat this as a PMM3 platykurtic case.
}
}
\examples{
data(auto_mpg)
# PMM2 example: MPG vs Acceleration (asymmetric residuals)
fit_ols <- lm(mpg ~ acceleration, data = auto_mpg)
pmm_skewness(residuals(fit_ols))  # gamma3 ~ 0.5 -> PMM2
pmm_dispatch(residuals(fit_ols))
fit_pmm2 <- lm_pmm2(mpg ~ acceleration, data = auto_mpg, na.action = na.omit)
coef(fit_pmm2)  # compare with coef(fit_ols)
}
\references{
Quinlan, J.R. (1993). Combining Instance-Based and Model-Based Learning.
In Proceedings on the Tenth International Conference of Machine Learning,
236-243.

Zabolotnii S., Warsza Z.L., Tkachenko O. (2018) Polynomial Estimation
of Linear Regression Parameters for the Asymmetric PDF of Errors.
Springer AISC, vol 743. \doi{10.1007/978-3-319-77179-3_75}
}
\keyword{datasets}
