A business wants to determine whether a relationship exists between the number of work-hours an employee misses per year (Y) and the employee’s annual wages (X), to test the hypothesis that increased compensation induces better work attendance. The data provided in the table below are based on a random sample of 15 employees from this organization.

Employee	Work Hours Missed	Annual Wages
1	155	8.8
2	127	8.3
3	72	10.0
4	6	15.8
5	36	14.5
6	56	21.0
7	63	10.8
8	11	17.2
9	485	12.2
10	48	13.1
11	79	9.7
12	91	10.2
13	191	7.8
14	34	11.5
15	82	10.9

Estimate a simple linear regression model using the sample data. How well does the estimated model fit the sample data?

At the 0.05 level, is the overall regression equation in part a significant?

Plot the fitted values versus residuals associated with the model in part a. What does the plot indicate?

Suppose you learn that the 9^th employee in the sample has been fired for missing an excessive number of work-hours during the past year. In light of this information, how would you proceed to estimate the relationship between the number of work-hours an employee misses per year and the employee’s annual wages, using the available information? If you decide to revise your estimate of this regression equation, repeat parts a and b.