Deans Dilemma Project
Srijani Das
February 3, 2018
This is my case study on Deans Dilemma Project which is to investigate on the following matters in R.
Read the data set into RStudio
deansdilemma.df <- read.csv(paste("Data - Deans Dilemma.csv", sep=""))
View(deansdilemma.df)Summarize the dataset
summary(deansdilemma.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(deansdilemma.df$Percent_SSC)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 64.65 10.96 64.5 64.76 12.6 37 87.2 50.2 -0.06 -0.72
## se
## X1 0.55describe(deansdilemma.df$Percent_HSC)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 63.8 11.42 63 63.34 13.34 40 94.7 54.7 0.29 -0.67
## se
## X1 0.58describe(deansdilemma.df$Percent_Degree)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 391 62.98 8.92 63 62.91 8.9 35 89 54 0.05 0.24 0.45describe(deansdilemma.df$Percentile_ET)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 54.93 31.17 62 56.87 25.2 0 98.69 98.69 -0.74 -0.69
## se
## X1 1.58describe(deansdilemma.df$S.TEST.SCORE)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 54.93 31.17 62 56.87 25.2 0 98.69 98.69 -0.74 -0.69
## se
## X1 1.58describe(deansdilemma.df$Percent_MBA)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 61.67 5.85 61.01 61.45 6.39 50.83 77.89 27.06 0.34 -0.52
## se
## X1 0.3describe(deansdilemma.df$Marks_Communication)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 391 60.54 8.82 58 59.68 8.9 50 88 38 0.74 -0.25 0.45describe(deansdilemma.df$Marks_Projectwork)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 68.36 7.15 69 68.6 7.41 50 87 37 -0.26 -0.27
## se
## X1 0.36describe(deansdilemma.df$Marks_BOCA)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 391 64.38 9.58 63 64.08 11.86 50 96 46 0.29 -0.85
## se
## X1 0.48describe(deansdilemma.df$Salary)## vars n mean sd median trimmed mad min max range skew
## X1 1 391 219078.3 138311.6 240000 217011.5 88956 0 940000 940000 0.24
## kurtosis se
## X1 1.74 6994.72Use R to calculate the median salary of all the students in the data sample
median(deansdilemma.df$Salary)## [1] 240000Use R to calculate the percentage of students who were placed
mytable <- with(deansdilemma.df, table(Placement))
mytable## Placement
## Not Placed Placed
## 79 312prop.table(mytable)*100## Placement
## Not Placed Placed
## 20.2046 79.7954Use R to create a dataframe called placed, that contains a subset of only those students who were successfully placed.
placed.df<-deansdilemma.df[which(deansdilemma.df$Placement_B==1),]
View(placed.df)Use R to find the median salary of students who were 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="lightblue")
Create a dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA.
notplaced.df<-deansdilemma.df[which(deansdilemma.df$Placement_B==0),]
View(notplaced.df)Draw two histograms side-by-side, visually comparing the MBA performance of Placed and Not Placed students,
par=(mfrow=c(2,1))
with(placed.df, hist(placed.df$Percent_MBA,
main="MBA Performance of placed students",
xlab="MBA Percentage",
ylab="Count",
xlim=c(50,80), ylim=c(0,150),
breaks=3,
col="lightblue"))
with(notplaced.df, hist(notplaced.df$Percent_MBA,
main="MBA Performance of not placed students",
xlab="MBA Percentage",
ylab="Count",
xlim=c(50,80),
breaks=3,
col="lightblue"))
Use R to draw two boxplots, one below the other, comparing the distribution of salaries of males and females who were placed
boxplot(Salary ~ Gender, data=placed.df, horizental=TRUE,
ylab="Salary", xlab="Gender",
main="Comparison of Salaries of Males and Females")
axis(side=2, at=c(1,2), labels=c("Females","Males"))
Create a dataframe called placedET, representing students who were placed after the MBA and who also gave some MBA entrance test before admission into the MBA program.
placedET.df<-deansdilemma.df[which(deansdilemma.df$Placement_B==1 & deansdilemma.df$S.TEST==1),]
View(placedET.df)Draw a Scatter Plot Matrix for 3 variables – {Salary, Percent_MBA, Percentile_ET} using the dataframe placedET.
library(car)
scatterplotMatrix(formula = ~ Salary + Percent_MBA + Percentile_ET, cex=0.6,
data=placedET.df, diagonal="density")