dilemma.df <-read.csv(paste("Data - Deans Dilemma.csv"),sep=",")
placed.df <- subset(dilemma.df,Placement_B==1)

task 3(d)

Q1.create a table showing the mean salary of males and females, who were placed.

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

Q2. What is the average salary of male MBAs who were placed?

 284241.9

Q3.What is the average salary of female MBAs who were placed?

 253068.0

Yes, there is gender gap in the data.

Q4. 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.”

null hypothesis : there is no significant difference in salaries of males and females.

library(MASS)
library(psych)
attach(placed.df)

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

Q5. What is the p-value based on the t-test?

p-value - 0.00234

Q6. Please interpret the meaning of the t-test, as applied to the average salaries of male and female MBAs.

since the p-value < 0.05 we can reject the null hypothesis. this concludes that there is significant difference in salaries of male and female MBAs.