Dean’s Dilemma

This is the analysis of data available on a B-school.

Median of Salary

The median of salary is:

median(dilemma.df$Salary)
## [1] 240000

Percentage of Students Placed

The percentage of students who were placed: correct to 2 decimal places.

mytable<-prop.table(table(dilemma.df$Placement_B))*100
format(round(mytable, 2), nsmall = 2)
## 
##       0       1 
## "20.20" "79.80"

Placed Table

placed <- dilemma.df[ which(dilemma.df$Placement_B=="1"), ]

Median Salary of Placed Students

The median salary of students who were placed:

 median(placed$Salary)
## [1] 260000

Mean Salary of Males and Females (Placed)

Table showing the mean salary of males and females, who were placed:

aggregate(placed$Salary,by=list(Gender=placed$Gender),mean)
##   Gender        x
## 1      F 253068.0
## 2      M 284241.9

Histogram on MBA Performance

 hist(placed$Percent_MBA,breaks = 3,col = "lightblue3",main = "MBA Performance of placed students",xlab = "MBA Percentage",ylab = "Count")

Not Placed Table

notplaced<-dilemma.df[which(dilemma.df$Placement_B=="0"),]

Comparision of Placed and Not Placed

hist(placed$Percent_MBA,breaks = 3,col = "lightblue3",main = "MBA Performance of placed students",xlab = "MBA Percentage",ylab = "Count")

hist(notplaced$Percent_MBA,breaks = 3,col = "lightblue3",main = "MBA Performance of notplaced students",xlab = "MBA Percentage",ylab = "Count")

Comparision of Salaries of Male and Females

boxplot(Salary ~ Gender, data=placed, horizontal=TRUE, yaxt="n", 
     ylab="Gender", xlab="Salary",
     main="Comparision of Salaries of Males and Females")
axis(side=2, at=c(1,2), labels=c("Females", "Males"))

Placed ET

placedET<-placed[which(placed$S.TEST=="1"),]

Scatter Plot Matrix

 scatterplotMatrix(formula = ~ Salary + Percent_MBA + Percentile_ET, cex=0.6,data=placedET,
                   diagonal="density",main="Sactter Plot Matrix")