Below is a residual plot for fitting a linear regression model: Residual n 10 00 09 40 4 50 What does the residual plot suggest about the model? (select all that apply) The variables are not linearly associated The linear regression model is not a good fit for the data The variables have a strong negative linear relationship The linear regression model is a good fit for the data

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Bookstore sales revisited Recall the data we saw inChapter 6, Exercise 3 for a bookstore. The manager wantsto predict Sales from Number of Sales People Working. Number of SalesPeople Working Sales (in $1000)2 103 117 139 1410 1810 2012 2015 2216 2220 26 Dependent variable is SalesR-squared = 93.2,s = 1.477 Variable CoefficientIntercept 8.1006Num_Workers 0.9134 a) Write the regression equation. Define the variablesused in your equation.b) What does the slope mean in this context?c) What does the y-intercept mean in this context? Is itmeaningful?d) If 18 people are working, what Sales do you predict?e) If sales for the 18 people are actually $25,000, what isthe value of the residual?f) Have we overestimated or underestimated the sales?