Deans Dilemma 2

store.df <- read.csv(paste("Deans Dilemma.csv", sep=""))
aggregate(store.df$Salary[store.df$Placement_B==1],by=list(Gender=store.df$Gender[store.df$Placement_B==1]),FUN = mean)

##   Gender        x
## 1      F 253068.0
## 2      M 284241.9

mean(store.df$Salary[store.df$Gender.B==0 & store.df$Placement_B==1])

## [1] 284241.9

mean(store.df$Salary[store.df$Gender.B==1 & store.df$Placement_B==1])

## [1] 253068

t.test(Salary[store.df$Placement_B==1] ~ Gender[store.df$Placement_B==1], data=store.df)

## 
##  Welch Two Sample t-test
## 
## data:  Salary[store.df$Placement_B == 1] by Gender[store.df$Placement_B == 1]
## t = -3.0757, df = 243.03, p-value = 0.00234
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -51138.42 -11209.22
## sample estimates:
## mean in group F mean in group M 
##        253068.0        284241.9