Dean.df <- read.csv(paste("DeansDilemma.csv", sep = ""))
  placed.df<-Dean.df[which(Dean.df$Placement_B== 1),]

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

  mean.salary <- aggregate(Salary ~ Gender, data= placed.df,mean )
  mean.salary
##   Gender   Salary
## 1      F 253068.0
## 2      M 284241.9

What is the average salary of male MBAs who were placed?

mean(placed.df$Salary[placed.df$Gender=="M"])
## [1] 284241.9

What is the average salary of female MBAs who were placed?

mean(placed.df$Salary[placed.df$Gender=="F"])
## [1] 253068

Submit 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.”

library(MASS)
library(psych)
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

What is the p-value based on the t-test?

P-value is 0.00234

Please interpret the meaning of the t-test, as applied to the average salaries of male and female MBAs.

All the calculation depends on the p-value which is very less (i.e p<0.05) so we will reject the null hypothesis.