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# Do not collaborate with anyone on anything. If you have any questions contact Corban.

INSTRUCTIONS TO CANDIDATES

1. Do not collaborate with anyone on anything. If you have any questions contact Corban. Any students collaborating on the exam are engaged in academic misconduct and will be given a grade of zero on the final exam, which will likely lead to a failing grade for the course.

2. Please include the questions in your submission. Keep the numbering and lettering the same or some other format that is very similar. The easiest way to do this is to type your answers into this document. I would greatly appreciate if your comments were in a different color such as blue.

3. You will need to determine the code to solve some of these problems. The code will be very similar to what you used on previous assignments.

4. Some of our work has been about diagnosing problems in a model such as outliers, determining if a response needs to be transformed or if there is a problem with some of the model assumptions. Unlike the homework, where I typically highlighted pretty clearly what needed to be done, you will need to address these issues on your own.

5. Include any plots that were helpful to you in reaching your conclusion.

6. Include any R output that was helpful in reaching your conclusions.

7. Regarding points 5 and 6, your final exam should be clear, clean and presented in a professional manner. Do not provide R output or plots without explanation.

8. Related, and possibly repeating myself, do not provide conclusions that were based on R output or plots without also providing the R output and plots.

9. The ability to cleanly and clearly present your work and incorporate the correct output will be part of the grading process.

Question 0: Please state your understanding of rule number 1 and that you did indeed follow this rule.

Question 1 (20 pts) (a) Provide the definition of a p-value.

(b) Is a p-value the probability the null hypothesis is true? Explain.

(c) Is a p-value the probability the null hypothesis is false? Explain.

Question 2 (20 pts) Provide an explanation of the standard error of a sample statistic, such as a coefficient from a regression model.

Question 3 (30 pts) The wage_data we have worked with this semester is from 1985. Say you have entered a time machine and are now working for a company in 1985. Say the wage_data set is the wages of employees from competitors of your company. You have been given the job of creating a model for wages in your company. The company’s policy is to provide equal pay regardless of race, gender, marital status or union membership. Use the wage data used to create a model for competitive wages for the company. Code to read in data set below.

a) Explain the model you have fit and the steps you have taken to get this final model.

b) What would the model estimate the hourly wages for someone with the following characteristics: (occupation: Sales, sector: Manufacturing, union: no, education: 16, experience: 10, age: 45, gender: male, marital status: yes, race: Hispanic, south: yes).

c) Say you have an exceptional employee, for instance one you would rank at the .975 percentile. Would you recommend using the model’s wage for this employee? Why or why not?

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