R

A table with the mean salary of males and females who were placed.

library(stats)
admi1 <- read.csv("Data - Deans Dilemma.csv")
Placed <- admi1[which(admi1$Placement_B==1),]
aggregate(Placed$Salary, by=list(sex= Placed$Gender), mean)
##   sex        x
## 1   F 253068.0
## 2   M 284241.9

Mean salary of males= 284241.9

Mean salary of females= 253068.0

t-test for the Hypothesis “The average salary of the male MBAs is higher than the average salary of female MBAs.”

Null hypothesis: There is no significant difference in average salary of MBA males and females.

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

First testing for all placed students:

t.test(Placed$Salary~Placed$Gender)
## 
##  Welch Two Sample t-test
## 
## data:  Placed$Salary by Placed$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

p-value: 0.00234

Tesing for all students:

t.test(admi1$Salary~admi1$Gender)
## 
##  Welch Two Sample t-test
## 
## data:  admi1$Salary by admi1$Gender
## t = -2.69, df = 278.55, p-value = 0.007577
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -66149.06 -10244.26
## sample estimates:
## mean in group F mean in group M 
##        193288.2        231484.8

p-value: 0.00757

In both the above tests, the p<0.05. Therefore we reject the null hypothesis. And conclude that the average salary of male MBA’s is higher is than the average salary of female MBAs.