R Dataset / Package DAAG / carprice

November 27, 2023

On this R-data statistics page, you will find information about the carprice data set which pertains to US Car Price Data. The carprice data set is found in the DAAG R package. You can load the carprice data set in R by issuing the following command at the console data("carprice"). This will load the data into a variable called carprice. If R says the carprice data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the carprice R data set. The size of this file is about 2,076 bytes.

US Car Price Data

Description

U.S. data extracted from Cars93, a data frame in the MASS package.

Usage

carprice

Format

This data frame contains the following columns:

Type: Type of car, e.g. Sporty, Van, Compact
Min.Price: Price for a basic model
Price: Price for a mid-range model
Max.Price: Price for a ‘premium’ model
Range.Price: Difference between Max.Price and Min.Price
RoughRange: Rough.Range plus some N(0,.0001) noise
gpm100: The number of gallons required to travel 100 miles
MPG.city: Average number of miles per gallon for city driving
MPG.highway: Average number of miles per gallon for highway driving

Source

MASS package

References

Venables, W.N.\ and Ripley, B.D., 4th edn 2002. Modern Applied Statistics with S. Springer, New York.

See also ‘R’ Complements to Modern Applied Statistics with S-Plus, available from http://www.stats.ox.ac.uk/pub/MASS3/

Examples

 
print("Multicollinearity - Example 6.8")
pairs(carprice[,-c(1,8,9)])carprice1.lm <- lm(gpm100 ~ Type+Min.Price+Price+Max.Price+Range.Price,
data=carprice)
round(summary(carprice1.lm)$coef,3)
pause()alias(carprice1.lm)
pause()carprice2.lm <- lm(gpm100 ~ Type+Min.Price+Price+Max.Price+RoughRange, data=carprice)
round(summary(carprice2.lm)$coef, 2)
pause()carprice.lm <- lm(gpm100 ~ Type + Price, data = carprice)
round(summary(carprice.lm)$coef,4)
pause()summary(carprice1.lm)$sigma # residual standard error when fitting all 3 price variables
pause()summary(carprice.lm)$sigma# residual standard error when only price is used
pause()vif(lm(gpm100 ~ Price, data=carprice)) # Baseline Price
pause()vif(carprice1.lm)# includes Min.Price, Price & Max.Price
pause()vif(carprice2.lm)# includes Min.Price, Price, Max.Price & RoughRange
pause()vif(carprice.lm) # Price alone

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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