Deans Dilemma study
Vashishtha Vidyarthi
6 January 2018
TASK 2b - Reading the dataset
Read the data set into RStudio
DDstudy.df<-read.csv(paste("Data - Deans Dilemma.csv", sep=""))
View(DDstudy.df)TASK 2c - Summarize the dataset
library(psych)
describe(DDstudy.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 6994.72TASK 3a
Use R to calculate the median salary of all the students in the data sample
attach(DDstudy.df)
median(Salary)## [1] 240000TASK 3b
Use R to calculate the percentage of students who were placed, correct to 2 decimal places
prop.table(with(DDstudy.df,table(Placement)))*100## Placement
## Not Placed Placed
## 20.2046 79.7954TASK 3c
Use R to create a dataframe called placed, that contains a subset of only those students who were successfully placed
placed.df<- DDstudy.df[which(Placement=='Placed'),]
View(placed.df)TASK 3d
Use R to find the median salary of students who were placed.
attach(placed.df)## The following objects are masked from DDstudy.df:
##
## Board_CBSE, Board_HSC, Board_ICSE, Board_SSC, Course_Degree,
## Degree_Engg, Entrance_Test, Experience_Yrs, Gender, Gender.B,
## Marks_BOCA, Marks_Communication, Marks_Projectwork,
## Percent_Degree, Percent_HSC, Percent_MBA, Percent_SSC,
## Percentile_ET, Placement, Placement_B, S.TEST, S.TEST.SCORE,
## Salary, SlNo, Specialization_MBA, Stream_HSCmedian(Salary)## [1] 260000TASK 3e
Use R to create a table showing the mean salary of males and females, who were placed.
gender_salary<-aggregate(Salary, by=list(Gender=Gender), mean)
gender_salary## Gender x
## 1 F 253068.0
## 2 M 284241.9TASK 3f
Use R to generate the following histogram showing a breakup of the MBA performance of the students who were placed
hist(Percent_MBA,
main="MBA Performance of Placed Students",
xlab="MBA Percentage",
ylab="Count",
xlim=c(50,80), ylim=c(0,150),
breaks=2,
col="gray")
TASK 3g
Create a dataframe called notplaced, that contains a subset of only those students who were NOT placed after their MBA
notplaced.df<-DDstudy.df[which(DDstudy.df$Placement=='Not Placed'),]TASK 3h
Draw two histograms side-by-side, visually comparing the MBA performance of Placed and Not Placed students, as follows:
par(mfrow=c(1,2))
hist(Percent_MBA,
main="MBA Performance of Placed Students",
xlab="MBA Percentage",
ylab="Count",
xlim=c(50,80), ylim=c(0,150),
breaks=2,
col="gray", cex.lab=0.7, cex.axis=0.7, cex.main=0.7, cex.sub=0.7)
hist(notplaced.df$Percent_MBA,
main="MBA Performance of Not Placed Students",
xlab="MBA Percentage",
ylab="Count",
xlim=c(50,80), ylim=c(0,40),
breaks=2,
col="gray", cex.lab=0.7, cex.axis=0.7, cex.main=0.7, cex.sub=0.7)
TASK 3i
Use R to draw two boxplots, one below the other, comparing the distribution of salaries of males and females who were placed, as follows:
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"))
TASK 3j
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<-placed.df[which(Entrance_Test!='None'),]
View(placedET.df)TASK 3k
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=0.6,
data=placedET.df)