Data...Deans.Dilemma <- read.csv("E:/Documents/internship-R/Data - Deans Dilemma.csv")
View(Data...Deans.Dilemma)
placed <- Data...Deans.Dilemma[ which(Data...Deans.Dilemma$Placement=='Placed'),]
View(placed)

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

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

What is the average salary of male MBAs who were placed?
284241.9

What is the average salary of female MBAs who were placed?
253068.0

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.”

t.test(Salary~Gender,Data...Deans.Dilemma)
## 
##  Welch Two Sample t-test
## 
## data:  Salary by 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

What is the p-value based on the t-test?
p-value = 0.007577

Please interpret the meaning of the t-test, as applied to the average salaries of male and female MBAs.
The p value is less than .05, therefore, there is a significant difference between the means of our sample population and we reject the null hypothesis. Thus, the alternative hypothesis is true. There is a significant difference between average salaries of males and females.