02.3 5 Points What is the total corrected sum of squares SStamg? Use all digits in your calculation and insert a number rounded to three decimals. Enter your answer here 02.4 15 Points Using regression diagnostics, describe three issues with the t of the model. For each issue, use up to three sentences and explicitly mention the plot you use to justify your answer. Enter your answer here A market analyst is studying quality characteristics of cars. More specifically, the analyst is investigating if the engine power of a car can be used to predict its miles per gallon (mpg). The engine power is measured in horsepower while mpg is the distance, measured in miles, that a car can travel per gallon of fuel. The analyst has a data set consisting of 392 observations of the variables of interest. More specifically, an observation in this set consists of the engine power and mpg of a car. To tackle the problem, the analyst uses linear regression as implemented in the statistical software R. The software output obtained by the analyst is displayed below. O C O O cars.data$Y O O 8 00 O o 8909 0O O 00 0 0 0 0 9090 0000 O O O 04 ) 0 0 00 o O O O C O O O O 50 100 150 200 cars.data$X> car. fit summary(car . fit) Call: Im(formula = Y ~ X, data = cars . data) Residuals: Min 1Q Median 3Q Max -13.5710 -3.2592 -0.3435 2.7630 16.9240 Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) 39.935861 0. 717499 55.66 predict(car. fit, data. frame(X = 98), interval = "confidence", level = 0.95) fit Lwr upr 1 24.46708 23.97308 24.96108 > predict(car. fit, data. frame(X = 98), interval = "prediction", level = 0.95) fit Lwr upr 1 24. 46708 14.8094 34.12476 Residuals vs Fitted Normal Q-Q 1210 0331 5 10 15 20 oog Residuals Standardized residuals -50 -2 -1 0 - 15 5 10 15 20 25 30 -3 -2 Fitted values Theoretical Quantiles Scale-Location Residuals vs Leverage 0331 1.5 1160 O 1.0 ViStandardized residuals Standardized residuals 19 0.5 Cook's distance 00 0.0 5 10 20 25 30 0.000 0.005 0.010 0.015 0.020 0.025 0.030 Fitted values Leverage