% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-mariokart.R
\docType{data}
\name{mariokart}
\alias{mariokart}
\title{Wii Mario Kart auctions from Ebay}
\format{
A data frame with 143 observations on the following 12 variables.
All prices are in US dollars.
\describe{
\item{id}{Auction ID assigned by Ebay.}
\item{duration}{Auction length, in days.}
\item{n_bids}{Number of bids.}
\item{cond}{Game condition, either \code{new} or \code{used}.}
\item{start_pr}{Start price of the auction.}
\item{ship_pr}{Shipping price.}
\item{total_pr}{Total price, which equals the auction price plus the
shipping price.}
\item{ship_sp}{Shipping speed or method.}
\item{seller_rate}{The seller's rating on Ebay. This is the number
of positive ratings minus the number of negative ratings for the seller.}
\item{stock_photo}{Whether the auction feature photo was a stock
photo or not. If the picture was used in many auctions, then it was called a
stock photo.}
\item{wheels}{Number of Wii wheels included in the auction. These are steering
wheel attachments to make it seem as though you are actually driving in the
game. When used with the controller, turning the wheel actually causes the
character on screen to turn.}
\item{title}{The title of the auctions.}
}
}
\source{
Ebay.
}
\usage{
mariokart
}
\description{
Auction data from Ebay for the game Mario Kart for the Nintendo Wii. This
data was collected in early October 2009.
}
\details{
There are several interesting features in the data. First off, note that
there are two outliers in the data. These serve as a nice example of what
one should do when encountering an outlier: examine the data point and
remove it only if there is a good reason. In these two cases, we can see
from the auction titles that they included other items in their auctions
besides the game, which justifies removing them from the dataset.

This dataset includes all auctions for a full week in October 2009.
Auctions were included in the dataset if they satisfied a number of
conditions. (1) They were included in a search for "wii mario kart" on
ebay.com, (2) items were in the Video Games > Games > Nintendo Wii section
of Ebay, (3) the listing was an auction and not exclusively a "Buy it Now"
listing (sellers sometimes offer an optional higher price for a buyer to end
bidding and win the auction immediately, which is an \emph{optional} Buy it
Now auction), (4) the item listed was the actual game, (5) the item was
being sold from the US, (6) the item had at least one bidder, (7) there were
no other items included in the auction with the exception of racing wheels,
either generic or brand-name being acceptable, and (8) the auction did not
end with a Buy It Now option.
}
\examples{

library(ggplot2)
library(broom)
library(dplyr)

# Identify outliers
ggplot(mariokart, aes(x = total_pr, y = cond)) +
  geom_boxplot()

# Replot without the outliers
mariokart |>
  filter(total_pr < 80) |>
  ggplot(aes(x = total_pr, y = cond)) +
  geom_boxplot()

# Fit a multiple regression models
mariokart_no <- mariokart |> filter(total_pr < 80)
m1 <- lm(total_pr ~ cond + stock_photo + duration + wheels, data = mariokart_no)
tidy(m1)
m2 <- lm(total_pr ~ cond + stock_photo + wheels, data = mariokart_no)
tidy(m2)
m3 <- lm(total_pr ~ cond + wheels, data = mariokart_no)
tidy(m3)

# Fit diagnostics
aug_m3 <- augment(m3)

ggplot(aug_m3, aes(x = .fitted, y = .resid)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(x = "Fitted values", y = "Residuals")

ggplot(aug_m3, aes(x = .fitted, y = abs(.resid))) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(x = "Fitted values", y = "Absolute value of residuals")

ggplot(aug_m3, aes(x = 1:nrow(aug_m3), y = .resid)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(x = "Order of data collection", y = "Residuals")

ggplot(aug_m3, aes(x = cond, y = .resid)) +
  geom_boxplot() +
  labs(x = "Condition", y = "Residuals")

ggplot(aug_m3, aes(x = wheels, y = .resid)) +
  geom_point() +
  labs(
    x = "Number of wheels", y = "Residuals",
    title = "Notice curvature"
  )
}
\keyword{datasets}
