Need excel formulas to solve this from a large sheet of data: Everpower Compensation Analysis

Everpower is a commercial real estate company with operations in $12$ cities in the southeastern

United States. Nick Harding is the new VP of Human Resources, and needs to understand the

current compensation practices. One of his chief concerns is the potential for discrimination

exposure.

The data can be accessed online. The deliverable is a Word document that answers the following

questions. Bullet point answers are appropriate. The document can be uploaded to Canvas.

Data definition notes:

$$ Job Category is coded: $1=$ Custodial; $2=$ Clerical, $3=$ Corporate.

$$ Education is coded:

o $1=$ Did not finish high school

o $2=$ Completed High School

o $3=$ Associate$$s Degree

o $4=$ Some $4-$year college

o $5=$ Bachelor$$s Degree

o $6=$ Graduate Degree

$$ Minority status is coded: $0=$ Non$-$minority, $1=$ minority

$$ Previous experience is the months of experience in the industry the employee had prior

to hire.

Case Questions:

$1.$ What is the average salary in this company?

$2.$ What is the average salary for men?

$3.$ What is the average salary for women?

$4.$ Conduct and interpret a t$-$test to determine if whether men and women$$s salaries

significantly differ.

$5.$ What is the average salary for racial minorities?

$6.$ What is the average salary for racial non$-$minorities?

$7.$ Conduct and interpret a t$-$test to determine if whether racial minority and non$-$minority

salaries significantly differ.

$8.$ There are significant differences in pay between which job categories?

$9.$ What is the correlation between level of education and job category? Is it significant?

$10.$ Is level of education significantly correlated with salary?

$11.$ Conduct a multivariate regression analysis. Which factor has the strongest relationships

with salary?

$12.$ Controlling for all available variables, what is the approximate average salary gain

expected from completing $1$ more degree?

$13.$ Based on this regression analysis, describe Everpower$$s exposure to a potential disparate

impact case for gender.

$14.$ Based on this regression analysis, describe Everpower$$s exposure to a potential disparate

impact case for race.