Assignment: Use RStudio to generate advanced graphs (using the ggplot2 package) using the dataset below. ***** Do not forget to tell RStudio what fields are factors! ***** Graphs to Produce: ggplot2
carprice R Documentation US Car Price Data Desc rip tio n U.S. data extracted from Cars93 , a data frame in the MASS package. U sa ge carprice F orm at 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 Dif ference 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 S ou rc e MASS package Refe re n ces V enables, 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/ Exam ple s 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
