Wage

You have received the following dataset containing information on your Fortune 500 company's wages for employees in different positions and locations across the country. The dataset contains a number of different variables. These variables include the average hourly wage of the employee (WAGE), the education level of the employee and their years of experience (EDUCATION AND EXPERIENCE), it also contains their tenure with the company, race, gender, marital status and number of dependents (TENURE, RACE, GENDER, MARRIED, and DEPENDENTS), as well as whether or not they are located in an urban or a rural facility (URBAN).

As an analyst working for your Fortune 500 company, you have been tasked with organizing, analyzing and interpreting this dataset in order to determine the relationship between hourly wages and the other variables of interest provided in the dataset.

Relate your regression results to the appropriately specified hypothesis tests regarding the independent variable and the dependent variable listed in part 6 above. Did the relationships you hypothesized in fact turn out to exist? If yes, explain why the relationships you hypothesized make sense. If no, explain why the relationships you hypothesized may not have shown up in the analysis. How does education appear to differ in its impact on wage relative to experience? Is more education or is more experience better?