Using R, build a regression model for data that interests you. Conduct residual analysis. Was the linear model appropriate? Why or why not?

I pick a data related to exp and salary,I personally hope this is a linear regression.

And I want to use plot to visualize the result.

From the scatter plot we can see it has a linear regression since it has a line relationship between salary and exp.

salary <- read.csv("/Users/jaylee/Downloads/Experience-Salary.csv", check.names = FALSE)
plot(salary$`salary(in thousands)`,salary$`exp(in months)`)

lmsalary = lm(`salary(in thousands)`~`exp(in months)`, data = salary)

The histogram show the same story.

expandsalary <- resid(lmsalary)
hist(expandsalary)

From all the plot below shows the date is normal distribution.

plot(salary, pch = 16, col = "blue") 
abline(lmsalary) 

plot(lmsalary)