Dean’s Dilemma
Tejashri Jayakanth
9 December 2017
The Dean’s Dilemma- A Statistical Analysis
Here, we analyse the placement statistics of Jain University of Management, as reported in the “Dean’s Dilemma” article published by Harvard Business Review.
The data set used is as follows:
setwd("C:\\Users\\Tejajay\\Desktop\\Internship\\3. Data Analytics")
dd <- read.csv(paste("DeansDilemma.csv", sep=""))
View(dd)Summary of Variables
Summary of the entire data set:
summary(dd)## SlNo Gender Gender.B Percent_SSC Board_SSC
## Min. : 1.0 F:127 Min. :0.0000 Min. :37.00 CBSE :113
## 1st Qu.: 98.5 M:264 1st Qu.:0.0000 1st Qu.:56.00 ICSE : 77
## Median :196.0 Median :0.0000 Median :64.50 Others:201
## Mean :196.0 Mean :0.3248 Mean :64.65
## 3rd Qu.:293.5 3rd Qu.:1.0000 3rd Qu.:74.00
## Max. :391.0 Max. :1.0000 Max. :87.20
##
## Board_CBSE Board_ICSE Percent_HSC Board_HSC
## Min. :0.000 Min. :0.0000 Min. :40.0 CBSE : 96
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:54.0 ISC : 48
## Median :0.000 Median :0.0000 Median :63.0 Others:247
## Mean :0.289 Mean :0.1969 Mean :63.8
## 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:72.0
## Max. :1.000 Max. :1.0000 Max. :94.7
##
## Stream_HSC Percent_Degree Course_Degree
## Arts : 18 Min. :35.00 Arts : 13
## Commerce:222 1st Qu.:57.52 Commerce :117
## Science :151 Median :63.00 Computer Applications: 32
## Mean :62.98 Engineering : 37
## 3rd Qu.:69.00 Management :163
## Max. :89.00 Others : 5
## Science : 24
## Degree_Engg Experience_Yrs Entrance_Test S.TEST
## Min. :0.00000 Min. :0.0000 MAT :265 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 None : 67 1st Qu.:1.0000
## Median :0.00000 Median :0.0000 K-MAT : 24 Median :1.0000
## Mean :0.09463 Mean :0.4783 CAT : 22 Mean :0.8286
## 3rd Qu.:0.00000 3rd Qu.:1.0000 PGCET : 8 3rd Qu.:1.0000
## Max. :1.00000 Max. :3.0000 GCET : 2 Max. :1.0000
## (Other): 3
## Percentile_ET S.TEST.SCORE Percent_MBA
## Min. : 0.00 Min. : 0.00 Min. :50.83
## 1st Qu.:41.19 1st Qu.:41.19 1st Qu.:57.20
## Median :62.00 Median :62.00 Median :61.01
## Mean :54.93 Mean :54.93 Mean :61.67
## 3rd Qu.:78.00 3rd Qu.:78.00 3rd Qu.:66.02
## Max. :98.69 Max. :98.69 Max. :77.89
##
## Specialization_MBA Marks_Communication Marks_Projectwork
## Marketing & Finance:222 Min. :50.00 Min. :50.00
## Marketing & HR :156 1st Qu.:53.00 1st Qu.:64.00
## Marketing & IB : 13 Median :58.00 Median :69.00
## Mean :60.54 Mean :68.36
## 3rd Qu.:67.00 3rd Qu.:74.00
## Max. :88.00 Max. :87.00
##
## Marks_BOCA Placement Placement_B Salary
## Min. :50.00 Not Placed: 79 Min. :0.000 Min. : 0
## 1st Qu.:57.00 Placed :312 1st Qu.:1.000 1st Qu.:172800
## Median :63.00 Median :1.000 Median :240000
## Mean :64.38 Mean :0.798 Mean :219078
## 3rd Qu.:72.50 3rd Qu.:1.000 3rd Qu.:300000
## Max. :96.00 Max. :1.000 Max. :940000
## Summary of SSC Percentage and Board:
summary(dd$Percent_SSC)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 37.00 56.00 64.50 64.65 74.00 87.20summary(dd$Board_SSC)## CBSE ICSE Others
## 113 77 201Summary of HSC Percentage, Board and Stream:
summary(dd$Percent_HSC)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 40.0 54.0 63.0 63.8 72.0 94.7summary(dd$Board_HSC)## CBSE ISC Others
## 96 48 247summary(dd$Stream_HSC)## Arts Commerce Science
## 18 222 151Summary of Degree course and Percentage:
summary(dd$Percent_Degree)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 35.00 57.52 63.00 62.98 69.00 89.00summary(dd$Course_Degree)## Arts Commerce Computer Applications
## 13 117 32
## Engineering Management Others
## 37 163 5
## Science
## 24Summary of Job experience:
summary(dd$Experience_Yrs)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4783 1.0000 3.0000Summary of Entrance Test details:
summary(dd$Entrance_Test)## CAT G-MAT G-SAT GCET K-MAT MAT None PGCET XAT
## 22 1 1 2 24 265 67 8 1summary(dd$S.TEST.SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.19 62.00 54.93 78.00 98.69summary(dd$Percentile_ET)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 41.19 62.00 54.93 78.00 98.69Summary of MBA Degree details:
summary(dd$Percent_MBA)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.83 57.20 61.01 61.67 66.02 77.89summary(dd$Specialization_MBA)## Marketing & Finance Marketing & HR Marketing & IB
## 222 156 13summary(dd$Marks_Communication)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.00 53.00 58.00 60.54 67.00 88.00summary(dd$Marks_Projectwork)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.00 64.00 69.00 68.36 74.00 87.00summary(dd$Marks_BOCA)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 50.00 57.00 63.00 64.38 72.50 96.00Summary of Placement details:
summary(dd$Placement)## Not Placed Placed
## 79 312summary(dd$Salary)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 172800 240000 219078 300000 940000Median salary of students:
median(dd$Salary)## [1] 240000Statistics of Students Who Were Placed
Percentage of students who were placed:
placedpercentage <- summary(dd$Placement)
View(placedpercentage)
prop.table(placedpercentage)*100## Not Placed Placed
## 20.2046 79.7954Overall data of students who were placed:
placed <- dd[ which(dd$Placement_B=='1'), ]
View(placed)Median salary of placed students:
median(placed$Salary)## [1] 260000Mean salary of male and female students:
placedmale <- placed[ which(placed$Gender.B=='0'), ]
mean(placedmale$Salary)## [1] 284241.9placedfemale <- placed[ which(placed$Gender.B=='1'), ]
mean(placedfemale$Salary)## [1] 253068Histogram of Placed Students:
hist(placed$Percent_MBA, main = "MBA Performance of Placed Students",
xlim = c(50, 80), ylim = c(0, 100),
xlab = "MBA Performance"
)
Not Placed Students:
notplaced <- dd[ which(dd$Placement_B=='0'), ]
View(notplaced)Histograms of Placed vs Non-placed:
par(mfrow=c(1,2))
hist(placed$Percent_MBA, xlab = "MBA Performance", main = "Placed")
hist(notplaced$Percent_MBA, xlab = "MBA Performance", main = "Not placed")
Boxplot of Male vs Female Salaries:
par(mfrow=c(2, 1))
boxplot(placedmale$Salary, main = "Male", xlab = "Salary")
boxplot(placedfemale$Salary, main = "Female", xlab = "Salary")
Entrance Exam + Placed:
placedET <- placed[ which(placed$S.TEST=='1'), ]
View(placedET)Scatterplots:
par(mfrow=c(1, 3))
plot(placedET$Salary, placedET$Percent_MBA, xlab = "Salary", ylab = "MBA Percentage")
plot(placedET$Salary, placedET$Percentile_ET, xlab = "Salary", ylab = "Entrance Test Percentile")
plot(placedET$Percent_MBA, placedET$Percentile_ET, xlab = "MBA Percentage", ylab = "Entrance Test Percentile")