Deans Dilemma
Poornachandra Rao
December 12, 2017
Deans Dilemma Case Study R Markdown Document
The following R Markdown document consists of analysis pertaining “Dean’s Dilemma Case Study.”
The scope of analysing this case is to bring an great efficacy in the both selection procedure and Placements of the students.
Firstly Entering the Data in to R,by creating a data frame “JainU_Data”
JainU_Data <- read.csv(paste("Data-Deans Dilemma.csv",sep = ""))
View(JainU_Data)
library(psych)
describe(JainU_Data)## 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 6994.72Median salary of All Students
median(JainU_Data$Salary)## [1] 240000Percentage of Students who where placed
Percent_Placed <- xtabs(~Placement,data=JainU_Data)
prop.table(Percent_Placed)*100## Placement
## Not Placed Placed
## 20.2046 79.7954Creating Dataframe, which contains subset_data of those students who were successfully placed
Placed <- JainU_Data[which(JainU_Data$Placement=='Placed'&JainU_Data$Placement_B=='1' ),]
View(Placed)Median Salary of Students who were Placed.
median(Placed$Salary)## [1] 260000Creating Table which depicts the Mean Salary of Males & Female Students, who were Placed.
aggregate(Placed$Salary,list(Gender=Placed$Gender),mean)## Gender x
## 1 F 253068.0
## 2 M 284241.9Histogram to depict the MBA Perfomance of Students.
hist(Placed$Percent_MBA,main = "MBA Perfomance of Placed Students",xlab="Percentage in MBA(%)",ylab="Frequency(No. of Counts)",xlim = c(50,80),ylim=c(0,50),breaks = 3,col ="yellow")
Creating Dataframe, which contains subset_data of those students who were not placed
Not_Placed <-JainU_Data[which(JainU_Data$Placement=='Not Placed'),]
View(Not_Placed) Comparing Histograms of Placed & Not_Placed Students
par(mfrow=c(1,2))
hist(Placed$Percent_MBA,main = "MBA Perfomance of Placed",xlab="Percentage in MBA(%)",ylab="Frequency(No. of Counts)",xlim = c(50,80),ylim=c(0,150),breaks = 3,col ="yellow")
hist(Not_Placed$Percent_MBA,main = "MBA Perfomance of Not Placed",xlab="Percentage in MBA(%)",ylab="Frequency(No. of Counts)",xlim = c(50,80),ylim=c(0,40),breaks =3 ,col ="yellow")
Drawing two boxplots, one below the other, comparing the distribution of salaries of males and females
boxplot(Placed$Salary~Placed$Gender,horizontal=TRUE,main="Comparison of salaries of Males and Females",xlab="salary",ylab="Gender",col=c("Yellow","green"),yaxt='n')
axis(side=2,at=c(1,2), labels =c('Females','Males'))
Create a dataframe “placedET”“, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program.
placedET <- JainU_Data[which(JainU_Data$Placement=='Placed' & JainU_Data$S.TEST=='1'),]
View(placedET)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(~ Salary+Percent_MBA+Percentile_ET, data=placedET)