When making predictions based on regression​ lines, which of the following is not listed as a​ consideration?     A. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables.   B. Use the regression line for predictions only if the data go far beyond the scope of the available sample data.   C. If the regression equation does not appear to be useful for making​ predictions, the best predicted value of a variable is its point estimate.   D. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the points reasonably well.

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12. A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce the following regression output that relates the city gas mileage (in mpg) and the engine displacement (in cubic inches). The regression equation is mpg:city= 33.4 0.0624 displacement Predictor Constant SE Coef T P 0.7762 43.00 0.000 displacement -0.0624 0.003810 0.000 S 3.13923 = R-Sq 66.0% R-Sq (adj) = 65.8% a. We have a car that has an engine with 150 cubic inches. Based on this output, what city gas mileage would you predict for this car? Coef 33.4 b. Based on this output what is the correlation between city gas mileage and displacement? C. I value: The test statistics for testing the slope is zero is missing. Calculate this The group o also recorded the power of the engine (in horsepower) for each car. The following regression output was produced. Predictor Constant horsepower The regression equation is mpg:city = 32.2 0.0572 horsepower Coef SE Coef 32.2 T…

. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below.       Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter   a. Dependent Variable: Delinquency b. All requested variables entered.     Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168   a. Predictors: (Constant), Family support     ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533     Total 12238.311 4532         a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…