QUESTION 25 Questions 17 - 25 concern the following idea: An economist is studying how many hours per week small business owners put into their business and whether it is related to the annual profit they earn from it. She gets information from a sample of small business owners about these two variables, and then runs a simple linear regression, some of the output of which is shown below. . Profit - last year's profit from the company (in 1000's of dollars) (ranges from 14.13 to 155.31) . Hours - the average weekly hours the small business owner personally worked for the business (ranges from 22 to 70) A simple linear regression is run, with the following output: summary(Im(profit ~ hours, data=small_bus)) Coefficients: Estimate Std. Error t value Pr(>It]) (Intercept) 0.9690 7.2469 0.134 0.894 hours 1.8551 0.2507 7.400 4.72e-11 Residual standard error: 19.8 on 98 degrees of freedom Multiple R-squared: 0.6074, Adjusted R-squared: 0.6034 F-statistic: 151.6 on 1 and 98 DF, p-value: 4.72e-11 Bob takes over the project and goes back and asks each of the small business owners their age. He wants to assess the impact of both hours and age in a multiple regression and see if they are linearly related to profit. He did STAT1520 many years ago and wants to use the method of backwards elimination and so this is what he does : 1) He fits a multiple regression with profit as the response variable and both hours and age as explanatory variables. 2) He checks the p-values of both hours and age to see if they are