dean<-read.csv(paste("Data - Deans Dilemma.csv",sep=" "))
View(dean)

TASK 3b

Use R to create a table showing the average salary of males and females, who were placed. Review whether there is a gender gap in the data. In other words, observe whether the average salaries of males is higher than the average salaries of females in this dataset.

placed<-dean[which(dean$Placement_B=="1"),]
mytable<-aggregate(placed$Salary,list(placed$Gender),mean)
mytable
##   Group.1        x
## 1       F 253068.0
## 2       M 284241.9

The Average salaries of Males is 284241.9.

The Average Salaries of Females is 253068.0

There is a difference in the average salaries in males and females.

TASK 3c

Use R to run a t-test to test the following hypothesis: H1: The average salary of the male MBAs is higher than the average salary of female MBAs.

Null Hypothesis

There is no significant difference between the salaries of males and females.

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

P-value is 0.00234. P-value<5% ,therefore we reject the null hypothesis.Hence we say that salary and gender is dependant.