Car Seats Analysis

Sameer Mathur

Analysis of Variance

Read the Car Seats Data

carSeats.df <- read.csv(paste("CarSeatsDataV5.csv", sep=""))
attach(carSeats.df)
dim(carSeats.df)

[1] 400  13

Group sample size

# group sample size
table(ShelveLoc)

ShelveLoc
   0-Bad 1-Medium   2-Good 
      96      219       85

Group means

# group means
meanProfit <- aggregate(Profit, by=list(ShelveLoc), FUN=mean)
colnames(meanProfit) <- c("Shelve Location","Average Profit")
meanProfit

  Shelve Location Average Profit
1           0-Bad       120.0414
2        1-Medium       163.3505
3          2-Good       232.5998

Group standard deviation

# group standard deviation 
sdProfit <- aggregate(Profit, by=list(ShelveLoc), FUN=sd)
colnames(sdProfit) <- c("Shelve Location","SD")
sdProfit

  Shelve Location       SD
1           0-Bad 44.96228
2        1-Medium 46.91608
3          2-Good 49.90406

BoxPlot of Profit broken down by ShelfLoc

boxplot(Profit ~ ShelveLoc, data=carSeats.df, main="Sales broken down by ShelfLoc",     xlab="Shelf Location", ylab="Sales ('000 units sold)")

plot of chunk unnamed-chunk-5

ANOVA

Test for group diffrences

fit <- aov(Profit ~ ShelveLoc)                                  
summary(fit)

             Df Sum Sq Mean Sq F value Pr(>F)    
ShelveLoc     2 580210  290105   130.7 <2e-16 ***
Residuals   397 881091    2219                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA

Test for group diffrences

fit <- aov(Profit ~ ShelveLoc + Advertising)                                  
summary(fit)

             Df Sum Sq Mean Sq F value   Pr(>F)    
ShelveLoc     2 580210  290105  153.33  < 2e-16 ***
Advertising   1 131834  131834   69.68 1.18e-15 ***
Residuals   396 749257    1892                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1