Recall the Data - Dean’s Dilemma.csv data file associated with the case “A Dean’s Dilemma: Selection of Students for the MBA Program”

dean.df <- read.csv(paste("Deans Dilemma.csv", sep =""))

Create a dataframe called placed, that contains a subset of only those students who were successfully placed.

placed <-dean.df[which(dean.df$Placement_B== "1"),]

1.R code that creates a table showing the mean salary of males and females, who were placed.

aggregate(placed$Salary, by=list(gender=placed$Gender), mean)
##   gender        x
## 1      F 253068.0
## 2      M 284241.9

2.The average salary of male MBAs who were placed=284241.9

aggregate(placed$Salary, by=list(gender=placed$Gender), mean)
##   gender        x
## 1      F 253068.0
## 2      M 284241.9

3.The average salary of female MBAs who were placed=253068

aggregate(placed$Salary, by=list(gender=placed$Gender), mean)
##   gender        x
## 1      F 253068.0
## 2      M 284241.9

4.R code to run a t-test for the Hypothesis “The average salary of the male MBAs is higher than the average salary of female MBAs.”

t.test(Salary~Gender, data=placed)
## 
##  Welch Two Sample t-test
## 
## data:  Salary by Gender
## 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

5.The p-value based on the t-test=0.00234

6.Interpretation of t-test

a)Male MBAs have a higher average salary at 284241.9 compared to female MBAs who have an average salary of 253068.

b)The t test showed there is a significant difference in average salary of the male and female MBAs who were placed(since p-value < 0.05)