QUESTION 23 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 Imagine the below pictures are the residual plots from this regression Residuals vs Fitted Normal Q-Q N Standardized residuals Residuals 100 150 200 Fitted values Theoretical QuantilesScale-Location 00 "Standardized residualsl 0.5 100 150 200 Fitted values Based on these plots, which of the following is the best conclusion regarding the linear regression assumptions. If you can't see graphs above, please click here (if that link doesn't work the graph is also on LMS, just underneath where the exam is) O a. The assumption of the independence of the residuals has been violated O b. The assumption of the normality of the residuals has been violated O c. The assumption of linear trend has been violated and we should try a polynomial model with time O d. Both the assumptions of the normality of the residuals and the independence of the residuals has been violated