Read data

MBA <- read.csv(file="Data - Deans Dilemma (1).csv", header=TRUE, sep=",")

subset of placed people

placed <- MBA[which(MBA$Placement_B == '1'),]

TASK-3d1:table of avarage salaries of both men and women.

aggregate(Salary ~ Gender, data = placed, mean)
##   Gender   Salary
## 1      F 253068.0
## 2      M 284241.9

TASK-3d2:avarage salary of male

placedm<-placed[which(placed$Gender=='M'),]

mean(placedm$Salary)
## [1] 284241.9

TASK-3d3:avarage salary of female

placedf<-placed[which(placed$Gender=='F'),]

mean(placedf$Salary)
## [1] 253068

TASK-3d4 Using T-test.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

TASK-3d5 P value based on the T test is 0.00234

TASK-3d6 The P-value is <0.05,thus we reject the null hypothesis that says there’s no significant difference between the average salaries.