Conclusion

We find that the majority of people rate 3 for job satisfaction. From the boxplot, we find that females have higher average salary than males. There’s no significant relationship between hours worked and job satisfaction, but the people who work more than 50 hours are significant less satisfied in their jobs.

Data Description

library(readxl)
survey2 <- read_excel("survey2.xlsx")
attach(survey2)
Sat<-(table(job_satisfaction)/length(job_satisfaction))
pie(Sat,col = c("blue","red","green","yellow"),main= "Job Satisfaction Distribution")

boxplot(salary2~gender,data=survey2, main="Salary by Gender", xlab="Gender", ylab="Salary")

hours <- factor(hours)
library(car)
## Loading required package: carData
boxplot(job_satisfaction~hours,data=survey2, main="Job Satisfaction by Hours Worked", xlab="Hours Worked", ylab="Job Satisfaction")

salary<-factor(salary2)
boxplot(job_satisfaction~salary2,data=survey2, main="Job Satisfaction by Salary", xlab="Salary", ylab="Job Satisfaction")

Hours2=as.numeric(as.factor(hours))
Salary2=as.numeric(as.factor(salary))
Satisfaction2=as.numeric(as.factor(job_satisfaction))

Satisfaction_Hours_Salary=data.frame(Satisfaction2,Hours2,Salary2)
Satisfaction_Hours_Salary
##    Satisfaction2 Hours2 Salary2
## 1              3      2       1
## 2              3      3       1
## 3              4      3       4
## 4              2      2       4
## 5              2      3       2
## 6              2      2       2
## 7              1      4       3
## 8              2      1       3
## 9              2      4       2
## 10             4      2       2
## 11             2      2       1
#Satisfaction and hours worked
plot(x = Hours2,y = Satisfaction2, main="Satisfaction and Hours worked",xlab="hours",ylab="satisfaction",col="blue")
abline(lm(Satisfaction2~Hours2),col="red")

#satisfaction and salary
plot(x = Salary2,y = Satisfaction2, main="Satisfaction and salary",xlab="Salary",ylab="satisfaction",col="blue")
abline(lm(Satisfaction2~Salary2),col="red")