The Dean’s Dilemna Case Study
Anshul Nograiya
3 February 2018
Loading the dataset
admi.df <- read.csv("Data - Deans Dilemma.csv")
View(admi.df)Summarize the dataset
summary(admi.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
## Median salary of all the students in the data sample
med <- median(admi.df$Salary)
med## [1] 240000Median salary of all the students in the data sample = 240000
Percentage of students who were Placed
perc <- (sum(admi.df$Placement_B)/nrow(admi.df))*100
round(perc, digits = 2)## [1] 79.8Percentage of students who were Placed = 79.80%
To create a new dataframe called placed, that contains a subset of only those students who were successfully placed
Placed <- admi.df[which(admi.df$Placement_B==1),]Median salary of students who were placed
median(Placed$Salary)## [1] 260000Median salary of students who were placed= 260000
To create a table showing the mean salary of males and females, who were placed
aggregate(Placed$Salary, by=list(sex= Placed$Gender), mean)## sex x
## 1 F 253068.0
## 2 M 284241.9To generate a histogram showing a breakup of the MBA performance of the students who were placed
hist(Placed$Percent_MBA,
main = "MBA Performance of Placed Students",
xlab = "MBA Percentage",
breaks = 3,
ylab = "Count",
col = "darkgrey")
To create a dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA
notplaced <- admi.df[which(admi.df$Placement_B==0),]To create a histogram showing comparision in the MBA performance of Placed and Not Placed students
par(mfrow =c(1,2))
hist(Placed$Percent_MBA,
main = "MBA Performance of Placed Students",
xlab = "MBA Percentage",
breaks = 3,
ylab = "Count",
col = "darkgrey")
hist(notplaced$Percent_MBA,
main = "MBA Performance of not Placed Students",
xlab = "MBA Percentage",
breaks = 3,
ylab = "Count",
col = "darkgrey")
par(mfrow =c(1,1))To draw boxplots showing the comparision of the distribution of salaries of males and females who were placed
boxplot(Placed$Salary~Placed$Gender.B, horizontal = TRUE,
yaxt="n", xlab= "Salary", ylab="Gender", las = 1)
axis(side = 2, at=c(1,2), labels=c("Males","Females"))
To 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 <- admi.df[which(admi.df$Placement_B ==1 & admi.df$S.TEST == 1),]To draw a Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET
library("car")## Warning: package 'car' was built under R version 3.3.3scatterplotMatrix(formula = ~PlacedET$Salary + PlacedET$Percent_MBA + PlacedET$Percentile_ET)