Dean’s Dilemma
Tamoghno Das
December 9, 2017
1.Synopsis
From among the applicants seeking admission to their MBA program, the B-schools had to identify those students who could successfully complete the course and also get placed eventually. Identifying potential students was a critical decision and it became imperative to identify the factors that could differentiate the two categories of students-those who could be placed easily and those who would struggle to get placed. Each year, the admissions committee of every B-school faced the tough task of screening the applicants and selecting those students who would eventually succeed in the MBA program. MBA admissions needed much more analytical reasoning, taking multiple criteria into consideration. The admissions team wanted to understand whether a student’s academic record would have any reflection on the placement status.
2.Exercise
Read the data set into RStudio
dilemma.df<-read.csv(paste("Data - Deans Dilemma.csv",sep=""))
View(dilemma.df)Create summary statistics (e.g. mean, standard deviation, median, mode) for the important variables in the dataset.
library(psych)
describe(dilemma.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 6994.72Use R to calculate the median salary of all the students in the data sample
median(dilemma.df$Salary)## [1] 240000Use R to calculate the percentage of students who were placed, correct to 2 decimal places.
mytable<-with(dilemma.df,table(Placement_B))
format(round(prop.table(mytable)*100,2), nsmall = 2)## Placement_B
## 0 1
## "20.20" "79.80"Use R to create a dataframe called placed, that contains a subset of only those students who were successfully placed.ALso Use R to find the median salary of students who were placed.
placed.df<-dilemma.df[which(dilemma.df$Placement=="Placed"),]
median(placed.df$Salary)## [1] 260000Use R to create a table showing the mean salary of males and females, who were placed.
aggregate(placed.df$Salary, by=list(Gender=placed.df$Gender),mean)## Gender x
## 1 F 253068.0
## 2 M 284241.9Use R to generate the following histogram showing a breakup of the MBA performance of the students who were placed
hist(placed.df$Percent_MBA, main = "MBA Performance of placed students",xlab = "MBA Percentage", ylab = "Count", xlim=c(50,80),ylim=c(0,150),breaks = 3, col = "gray")
Create a dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA.
notplaced.df<-dilemma.df[which(dilemma.df$Placement=="Not Placed"),]Draw two histograms side-by-side, visually comparing the MBA performance of Placed and Not Placed students, as follows:
par(mfrow=c(1,2))
with(placed.df,hist(placed.df$Percent_MBA, main="MBA Performance of placed students", xlab = "MBA Percentage", ylab = "Count", xlim = c(50,80), ylim = c(0,150), breaks = 3,col = "gray"))
with(notplaced.df,hist(notplaced.df$Percent_MBA, main = "MBA Performance of not placed students", xlab = "MBA Percentage", ylab = "Count", xlim = c(50,80), ylim = c(0,30), breaks = 3, col = "gray"))
Use R to draw two boxplots, one below the other, comparing the distribution of salaries of males and females who were placed, as follows:
par(mfrow=c(1,1))
boxplot(Salary~Gender,data = placed.df, horizontal = TRUE, yaxt="n",ylab="Gender", xlab="Salary", main="Comparison of Salaries of Males and Females")
axis(side=2,at=c(1,2), labels=c("Females", "Males"))
Create a dataframe called placedET, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program.
placedET.df<-dilemma.df[which(dilemma.df$Placement=="Placed"&dilemma.df$S.TEST==1),]Draw a Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplotMatrix(formula=~Salary+Percent_MBA+Percentile_ET, cex=1.2, data=placedET.df,diagonal="density")
Conclusion
The scores of a candidate during his/her MBA,along with his/her percentile score in the Entrance Test play a decisive role in whether or not that candidate is going to be placed. So, these parameters must be kept in mind while granting admission to a candidate.