Task2b

DDdata <- read.csv(“G://IIM Lucknow//Data - Deans Dilemma.csv”) #Task2c summary(DDdata) library(psych) describe(DDdata) #Task3a median_salary <- median(DDdata\(Salary) View(median_salary) #Task3b table(DDdata\)Placement) percentage_placed <- prop.table(table(DDdata\(Placement))*100 View(percentage_placed) #Task3c placed_students <- DDdata[ which(DDdata\)Placement== ‘Placed’),] #Task3d median_salary_placed <- median(placed_students\(Salary) View(median_salary_placed) #Task3e library(dplyr) meansalary <- aggregate(placed_students\)Salary, by = list(placed_students\(Gender), mean) #Task3f hist(placed_students\)Percent_MBA, main = “MBA performance of placed students” , xlab = “MBA Percentage”, ylab = “Count”, col = “lightblue”, xlim = c(50,80), ylim = c(0,50), breaks = 10) #Task3g notplaced <- DDdata[ which(DDdata$Placement==‘Not Placed’),] #Task3h par(mfrow=c(1,2)) hist(placed_students\(Percent_MBA, main = "MBA performance of placed students", xlab = "MBA Percentage", ylab = "Count", col = "lightblue", xlim = c(50,80), ylim = c(0,50), breaks = 10) hist(notplaced\)Percent_MBA, main = “MBA performance of not placed studenrs”, xlab = “MBA Percentage”, ylab = “Count”, col = “darkblue”, xlim = c(50,80), ylim = c(0,30), breaks = 10) #Task3i boxplot(Salary~Gender, data = placed_students, 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”)) #Task3j placed_with_entrance_test <- DDdata[which(DDdata\(Placement == "Placed" & DDdata\)S.TEST == 1),] #Task3k

install.packages(“car”) library(car) scatterplotMatrix(formula = ~Salary+Percent_MBA+Percentile_ET, cex = 0.6, data = placed_with_entrance_test, diagonal = “histogram”)