I have provided you a data set with1419 observations on salariesalong with a number of other variables. You work is to generate and interpret a multiple linear regression with salary as a function of age (variable age), education (variable educ), gender (variable female), minority status (variable minority), and time on the job (variable jobtime) Before running that model, you should look at these variables individually and provide detail for each: possible statistics might be the mean values, minimums, maximums, etc. The idea is to describe these data to me, your reader. Because salary is the crucial variable, you might want to look at dependent variable salary and how it relates to each of these variables independently:

1. how do salary and educ relate (what is the statistic used? Why? What does it mean?)
2. how do salary and jobtime relate (what is the statistic used? Why? What does it mean?)
3. how do salary and age relate (what is the statistic used? Why? What does it mean?)
4. how do salary and female relate (what is the statistic used? Why? What does it mean?)
5. how do salary and minority relate (what is the statistic used? Why? What does it mean?
6. how do salary and jobcat (job category) relate (what is the statistic used? Why? What does it mean?

Finally, generate a regression model using all of the variables discussed in the introduction and discuss the output(salary as a function of age (variable age), education (variable educ), gender (variable female), minority status (variable minority), and time on the job (variable jobtime)). (do not include information on job category in the regression for now)

Make sure to speak about each of the coefficients: for example,

• what is the coefficient on education?
• What does it mean/how do you interpret it?
• Is it significant?
• How do you know?, and so forth.