ddilemma.df <- read.csv(paste("Data - Deans Dilemma.csv", sep=""))
attach(ddilemma.df)   # attaching the columns of the data frame
library(psych)

STUDENTS WHO WERE SUCCESSFULLY PLACED:

placed.df <- ddilemma.df[ which(ddilemma.df$Placement=='Placed'), ]
boxplot(Salary ~ Gender, data = placed.df, xlab= "Gender", ylab= "average salary", horizontal= TRUE)

Mean salary of males and females, who were placed

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

According to above table, there is a gender gap between the average salary of males and the average salary of females.

Hypothesis 1

H1: The average salary of the male MBAs is higher than the average salary of female MBAs.

 t.test(Salary ~ Gender, data = placed.df)
## 
##  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

Result of Independent t-test

Based on the above output of the t-test, we can reject the hypothesis that the average salary of males is equal the average salary of females.(p<0.001)

What is the average salary of male MBAs who were placed? 284241.90 What is the average salary of female MBAs who were placed? 253068.00 What is the p-value based on the t-test? 0.00234 The t test showed there was a significant difference in the average salaries of males and the average salaries of females.