Suppose you are tasked with analyzing productivity data for a firm that produces outsourced reports for other companies. The first few rows of the data look like this, where reports is the number of reports produced in an observed week, num_employees is the number of employees who worked that week and total_employee_hours is the number of combined hours worked by those employees that week:

We run a regression on the full dataset and obtain the following output: Regression Statistics Multiple R 0.92 R Square 0.84 Adjusted R Square 0.83 Standard Error 150.40 Observations 50 ANOVA df SS MS F Significance F Regression 2 5563450.267 2781725.13 ? 2.11241E-19 Residual 47 1063167.413 22620.58 Total 49 6626617.68 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -66.68 126.00 -0.53 0.599127113 -320.15 186.79 num_employees 26.35 10.11 2.61 0.012243379 6.01 46.69 total_employee_hours 2.12 0.20 10.50 6.44059E-14 1.72 2.53reports num_employees total_employee_hours 1441 19 543 1517 16 524 1385 12 505 1518 11 521 2209 19 720 1384 11 549