Dean’s Dilemma-Selection of students for the MBA program
Aswathy Gunadeep
9 December 2017
The Dean’s Dilemma Case study
This is a case study based on the case “Dean’s Dilemma: Selection of Students for the MBA Program”.
Reading and Viewing the Dean’s Dilemma Database
setwd("D:/desktop/Data Analytics internship-sameer mathur/work/datasets")
mba.df<-read.csv(paste("Data - Deans Dilemma.csv", sep=""))
View(mba.df)Summary statistics of Dean’s Dilemma Data-database.
summary(mba.df)## 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
## library(psych)
describe(mba.df)## vars n mean sd median trimmed
## SlNo 1 391 196.00 113.02 196.00 196.00
## Gender* 2 391 1.68 0.47 2.00 1.72
## Gender.B 3 391 0.32 0.47 0.00 0.28
## Percent_SSC 4 391 64.65 10.96 64.50 64.76
## Board_SSC* 5 391 2.23 0.87 3.00 2.28
## Board_CBSE 6 391 0.29 0.45 0.00 0.24
## Board_ICSE 7 391 0.20 0.40 0.00 0.12
## Percent_HSC 8 391 63.80 11.42 63.00 63.34
## Board_HSC* 9 391 2.39 0.85 3.00 2.48
## Stream_HSC* 10 391 2.34 0.56 2.00 2.36
## Percent_Degree 11 391 62.98 8.92 63.00 62.91
## Course_Degree* 12 391 3.85 1.61 4.00 3.81
## Degree_Engg 13 391 0.09 0.29 0.00 0.00
## Experience_Yrs 14 391 0.48 0.67 0.00 0.36
## Entrance_Test* 15 391 5.85 1.35 6.00 6.08
## S.TEST 16 391 0.83 0.38 1.00 0.91
## Percentile_ET 17 391 54.93 31.17 62.00 56.87
## S.TEST.SCORE 18 391 54.93 31.17 62.00 56.87
## Percent_MBA 19 391 61.67 5.85 61.01 61.45
## Specialization_MBA* 20 391 1.47 0.56 1.00 1.42
## Marks_Communication 21 391 60.54 8.82 58.00 59.68
## Marks_Projectwork 22 391 68.36 7.15 69.00 68.60
## Marks_BOCA 23 391 64.38 9.58 63.00 64.08
## Placement* 24 391 1.80 0.40 2.00 1.87
## Placement_B 25 391 0.80 0.40 1.00 0.87
## Salary 26 391 219078.26 138311.65 240000.00 217011.50
## mad min max range skew kurtosis
## SlNo 145.29 1.00 391.00 390.00 0.00 -1.21
## Gender* 0.00 1.00 2.00 1.00 -0.75 -1.45
## Gender.B 0.00 0.00 1.00 1.00 0.75 -1.45
## Percent_SSC 12.60 37.00 87.20 50.20 -0.06 -0.72
## Board_SSC* 0.00 1.00 3.00 2.00 -0.45 -1.53
## Board_CBSE 0.00 0.00 1.00 1.00 0.93 -1.14
## Board_ICSE 0.00 0.00 1.00 1.00 1.52 0.31
## Percent_HSC 13.34 40.00 94.70 54.70 0.29 -0.67
## Board_HSC* 0.00 1.00 3.00 2.00 -0.83 -1.13
## Stream_HSC* 0.00 1.00 3.00 2.00 -0.12 -0.72
## Percent_Degree 8.90 35.00 89.00 54.00 0.05 0.24
## Course_Degree* 1.48 1.00 7.00 6.00 0.00 -1.08
## Degree_Engg 0.00 0.00 1.00 1.00 2.76 5.63
## Experience_Yrs 0.00 0.00 3.00 3.00 1.27 1.17
## Entrance_Test* 0.00 1.00 9.00 8.00 -2.52 7.04
## S.TEST 0.00 0.00 1.00 1.00 -1.74 1.02
## Percentile_ET 25.20 0.00 98.69 98.69 -0.74 -0.69
## S.TEST.SCORE 25.20 0.00 98.69 98.69 -0.74 -0.69
## Percent_MBA 6.39 50.83 77.89 27.06 0.34 -0.52
## Specialization_MBA* 0.00 1.00 3.00 2.00 0.70 -0.56
## Marks_Communication 8.90 50.00 88.00 38.00 0.74 -0.25
## Marks_Projectwork 7.41 50.00 87.00 37.00 -0.26 -0.27
## Marks_BOCA 11.86 50.00 96.00 46.00 0.29 -0.85
## Placement* 0.00 1.00 2.00 1.00 -1.48 0.19
## Placement_B 0.00 0.00 1.00 1.00 -1.48 0.19
## Salary 88956.00 0.00 940000.00 940000.00 0.24 1.74
## se
## SlNo 5.72
## Gender* 0.02
## Gender.B 0.02
## Percent_SSC 0.55
## Board_SSC* 0.04
## Board_CBSE 0.02
## Board_ICSE 0.02
## Percent_HSC 0.58
## Board_HSC* 0.04
## Stream_HSC* 0.03
## Percent_Degree 0.45
## Course_Degree* 0.08
## Degree_Engg 0.01
## Experience_Yrs 0.03
## Entrance_Test* 0.07
## S.TEST 0.02
## Percentile_ET 1.58
## S.TEST.SCORE 1.58
## Percent_MBA 0.30
## Specialization_MBA* 0.03
## Marks_Communication 0.45
## Marks_Projectwork 0.36
## Marks_BOCA 0.48
## Placement* 0.02
## Placement_B 0.02
## Salary 6994.72There are 390 entries in total. The average salary of a student is 219078.26 rupees.
3a: median salary of all the students
median(mba.df$Salary)## [1] 240000The median is 240000 rupees.
3b: the percentage of students who were placed
mytable <- xtabs(~ Placement_B, data=mba.df)
mytable## Placement_B
## 0 1
## 79 312format(round((prop.table(mytable)*100), 2), nsmall = 2)## Placement_B
## 0 1
## "20.20" "79.80"79.80% of the students were placed.
3c: Creating and viewing a dataframe called ‘placed’, that contains a subset of only those students who were successfully placed
placed.df <- subset(mba.df, mba.df$Placement_B==1 )
View(placed.df)3d: median salary of students who were placed
median(placed.df$Salary)## [1] 260000The median salary of students who were placed is 260000 rupees.
3e: table showing the mean salary of males and females, who were placed
placed.salary.mean <- aggregate(placed.df$Salary, list(placed.df$Gender), mean)
colnames(placed.salary.mean)[2] <- "Mean Salary"
colnames(placed.salary.mean)[1] <- "Gender"
placed.salary.mean## Gender Mean Salary
## 1 F 253068.0
## 2 M 284241.9The mean salary of females who were placed is 253068.0 rupees and that of males who were placed is 284241.9 rupees.
3f: histogram showing a breakup of the MBA performance of the students who were placed
hist(placed.df$Percent_MBA, main="MBA Performance of placed students", ylab="Count", xlab="MBA Percentage", col=c("red","blue","green","yellow"), breaks=20, xlim=c(50,80), ylim=c(0,30))
From the graph, maximum number of students have got an average percentage of 62-63% during their mba.
3g: Creating and viewing a dataframe called ‘notplaced’, that contains a subset of only those students who were NOT placed after their MBA
notplaced.df <- subset(mba.df, mba.df$Placement_B==0 )
View(notplaced.df)3h: comparison of the MBA performance of Placed and Not Placed students
par(mfrow=c(1,2))
hist(placed.df$Percent_MBA, main="MBA Performance of placed students", ylab="Count", xlab="MBA Percentage", col="turquoise", breaks=20, xlim=c(50,80), ylim=c(0,30))
hist(notplaced.df$Percent_MBA, main="MBA Performance of NOT placed students", ylab="Count", xlab="MBA Percentage", col="turquoise", breaks=20, xlim=c(50,80), ylim=c(0,8))
From the graph, it is clear that the performance of placed MBA students is just a bit better than that of NOT placed MBA students.
3i: comparison of the distribution of salaries of males and females who were placed
boxplot(Salary ~ Gender, data=placed.df, yaxt="n", horizontal = TRUE, ylab="Gender",
xlab="Salary", main="Comparison of salaries of Males and Females", col=c("yellowgreen", "pink"))
axis(side=2, at=c(1,2), labels=c("Males" , "Females"))
From the boxplot, the females who are placed have higher salaries than males. The median salary of females are also higher than that of males who are placed.
3j: Creating and viewing a dataframe called ‘placedET’, representing students who were placed after completing MBA and who also gave some MBA entrance test before admission into the MBA program
placedET.df <- subset(mba.df, mba.df$Placement_B==1 & mba.df$S.TEST==1)
View(placedET.df)3k: Scatter Plot Matrix for 3 variables: salary, percentage obtained in mba program and percentile obtained in mba entrance test
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplotMatrix(formula= ~ Salary+Percent_MBA+Percentile_ET, cex=0.6, data=placedET.df, diagonal="density", main="Scatter Plot Matrix")
From the scatterplot, it is clear that students who have took an entrance test have performed better during their MBA program.