In last document, we analysed data regarding case of Dean’s Dilemma. We saw that Dean is facing a problem to select right parameter to judge a future MBA aspairents. We will expand our study to different dimentions so that we can arriave at a concrete decision on selection parameters.
setwd("C:/Users/Abhi/Desktop/Data Analytics/Week 2 Day 1/")
dd<-read.csv(paste("Data - Deans Dilemma.csv", sep=""))
head(dd)## SlNo Gender Gender.B Percent_SSC Board_SSC Board_CBSE Board_ICSE
## 1 1 M 0 62.00 Others 0 0
## 2 2 M 0 76.33 ICSE 0 1
## 3 3 M 0 72.00 Others 0 0
## 4 4 M 0 60.00 CBSE 1 0
## 5 5 M 0 61.00 CBSE 1 0
## 6 6 M 0 55.00 ICSE 0 1
## Percent_HSC Board_HSC Stream_HSC Percent_Degree Course_Degree
## 1 88.00 Others Commerce 52.00 Science
## 2 75.33 Others Science 75.48 Computer Applications
## 3 78.00 Others Commerce 66.63 Engineering
## 4 63.00 CBSE Arts 58.00 Management
## 5 55.00 ISC Science 54.00 Engineering
## 6 64.00 CBSE Commerce 50.00 Commerce
## Degree_Engg Experience_Yrs Entrance_Test S.TEST Percentile_ET
## 1 0 0 MAT 1 55.0
## 2 0 1 MAT 1 86.5
## 3 1 0 None 0 0.0
## 4 0 0 MAT 1 75.0
## 5 1 1 MAT 1 66.0
## 6 0 0 None 0 0.0
## S.TEST.SCORE Percent_MBA Specialization_MBA Marks_Communication
## 1 55.0 58.80 Marketing & HR 50
## 2 86.5 66.28 Marketing & Finance 69
## 3 0.0 52.91 Marketing & Finance 50
## 4 75.0 57.80 Marketing & Finance 54
## 5 66.0 59.43 Marketing & HR 52
## 6 0.0 56.81 Marketing & Finance 53
## Marks_Projectwork Marks_BOCA Placement Placement_B Salary
## 1 65 74 Placed 1 270000
## 2 70 75 Placed 1 200000
## 3 61 59 Placed 1 240000
## 4 66 62 Placed 1 250000
## 5 65 67 Placed 1 180000
## 6 70 53 Placed 1 300000Creating directory for placed students.
placed.df<-dd[which(dd$Placement_B=='1'),]
head(placed.df)## SlNo Gender Gender.B Percent_SSC Board_SSC Board_CBSE Board_ICSE
## 1 1 M 0 62.00 Others 0 0
## 2 2 M 0 76.33 ICSE 0 1
## 3 3 M 0 72.00 Others 0 0
## 4 4 M 0 60.00 CBSE 1 0
## 5 5 M 0 61.00 CBSE 1 0
## 6 6 M 0 55.00 ICSE 0 1
## Percent_HSC Board_HSC Stream_HSC Percent_Degree Course_Degree
## 1 88.00 Others Commerce 52.00 Science
## 2 75.33 Others Science 75.48 Computer Applications
## 3 78.00 Others Commerce 66.63 Engineering
## 4 63.00 CBSE Arts 58.00 Management
## 5 55.00 ISC Science 54.00 Engineering
## 6 64.00 CBSE Commerce 50.00 Commerce
## Degree_Engg Experience_Yrs Entrance_Test S.TEST Percentile_ET
## 1 0 0 MAT 1 55.0
## 2 0 1 MAT 1 86.5
## 3 1 0 None 0 0.0
## 4 0 0 MAT 1 75.0
## 5 1 1 MAT 1 66.0
## 6 0 0 None 0 0.0
## S.TEST.SCORE Percent_MBA Specialization_MBA Marks_Communication
## 1 55.0 58.80 Marketing & HR 50
## 2 86.5 66.28 Marketing & Finance 69
## 3 0.0 52.91 Marketing & Finance 50
## 4 75.0 57.80 Marketing & Finance 54
## 5 66.0 59.43 Marketing & HR 52
## 6 0.0 56.81 Marketing & Finance 53
## Marks_Projectwork Marks_BOCA Placement Placement_B Salary
## 1 65 74 Placed 1 270000
## 2 70 75 Placed 1 200000
## 3 61 59 Placed 1 240000
## 4 66 62 Placed 1 250000
## 5 65 67 Placed 1 180000
## 6 70 53 Placed 1 300000Average salary of male and female studnets.
aggregate(Salary~Gender,data = placed.df,mean)## Gender Salary
## 1 F 253068.0
## 2 M 284241.9From above table, we can see that average salary for male MBAs is 2,84,241.9 while for female MBAs is 2,53,068.0 . We can use T-test to check whether this difference in salary paid to male and female MBAs is significant or not. Null Hypothesis: “The average salary of the male MBAs is equal to the average salary of female MBAs.” Alternate hypothesis: “The average salary of the male MBA is higher than the average salary of female MBAs.”
t.test(placed.df$Salary~placed.df$Gender,var.equal = TRUE)##
## Two Sample t-test
##
## data: placed.df$Salary by placed.df$Gender
## t = -2.7597, df = 310, p-value = 0.00613
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -53400.627 -8947.012
## sample estimates:
## mean in group F mean in group M
## 253068.0 284241.9Here, we can see that p(0.00613) value is less than 0.05. Hence, this we reject null hypotheis. Which means, there is a significant difference between salary of male and female students. One conclusion can be drawn from this test is that Admission committe should look increase intake of male candidates. However, we will look to other dimensions as well before coming to a final conclusion.