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Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9 -0.00749x, where x represe weight. Complete parts (a) through (d). Click the icon to view the car data. a. What does the symbol y represent? OA. y represents the actual value of highway fuel consumption. OB. y represents the predicted value of weight. O c. y represents the actual value of weight. OD. y represents the predicted value of highway fuel consumption. b. What are the specific values of the slope and y-intercept of the regression line? OA. The slope is 58.9 and the y-intercept is 0.007499. OB. The slope is -0.00749 and the y-intercept is 58.9. OC. The slope is 0.00749 and the y-intercept is 58.9. OD. The slope is 58.9 and the y-intercept is -0.00749. c. What is the predictor variable? OA. The predictor variable is highway fuel consumption, which is represented by y. OB. The predictor variable is weight, which is represented by x. OC. The predictor variable is weight, which is represented by y. OD. The predictor variable is highway fuel consumption, which is represented by x. Car Data Weight Highway Fuel Consumption 2844 36 3109 38 41 33 42 31 40 37 35 40 2870 3095 2915 2985 2563 3009 2798 6100 2468 2598 2558 3452 3514 Print 38 37 36 32 Done d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2997 lb? The best predicted value of highway fuel consumption of a car that weighs 2997 lb is mi/gal. (Round to one decimal place as needed.) -

Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9 -0.00749x, where x represe weight. Complete parts (a) through (d). Click the icon to view the car data. a. What does the symbol y represent? OA. y represents the actual value of highway fuel consumption. OB. y represents the predicted value of weight. O c. y represents the actual value of weight. OD. y represents the predicted value of highway fuel consumption. b. What are the specific values of the slope and y-intercept of the regression line? OA. The slope is 58.9 and the y-intercept is 0.007499. OB. The slope is -0.00749 and the y-intercept is 58.9. OC. The slope is 0.00749 and the y-intercept is 58.9. OD. The slope is 58.9 and the y-intercept is -0.00749. c. What is the predictor variable? OA. The predictor variable is highway fuel consumption, which is represented by y. OB. The predictor variable is weight, which is represented by x. OC. The predictor variable is weight, which is represented by y. OD. The predictor variable is highway fuel consumption, which is represented by x. Car Data Weight Highway Fuel Consumption 2844 36 3109 38 41 33 42 31 40 37 35 40 2870 3095 2915 2985 2563 3009 2798 6100 2468 2598 2558 3452 3514 Print 38 37 36 32 Done d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2997 lb? The best predicted value of highway fuel consumption of a car that weighs 2997 lb is mi/gal. (Round to one decimal place as needed.) -

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
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Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9 -0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. a. What does the symbol y represent? O A. y represents the actual value of highway fuel consumption. OB. y represents the predicted value of weight. OC. y represents the actual value of weight. OD. y represents the predicted value of highway fuel consumption. b. What are the specific values of the slope and y-intercept of the regression line? OA. The slope is 58.9 and the y-intercept is 0.007499. OB. The slope is -0.00749 and the y-intercept is 58.9. OC. The slope is 0.00749 and the y-intercept is 58.9. O D. The slope is 58.9 and the y-intercept is -0.00749. c. What is the predictor variable? OA. The predictor variable is highway fuel consumption, which is represented by y. OB. The predictor variable is weight, which is represented by x. OC. The predictor variable is weight, which is represented by y. OD. The predictor variable is highway fuel consumption, which is represented by x. (...) Car Data Weight Highway Fuel Consumption 2844 36 3109 38 41 33 42 31 40 2870 3095 2915 2985 2563 3009 2798 2468 2598 2558 3452 3514 Print 37 35 40 38 37 36 32 Done d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2997 lb? mi/gal. The best predicted value of highway fuel consumption of a car that weighs 2997 lb is (Round to one decimal place as needed.) - X
Transcribed Image Text:Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9 -0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. a. What does the symbol y represent? O A. y represents the actual value of highway fuel consumption. OB. y represents the predicted value of weight. OC. y represents the actual value of weight. OD. y represents the predicted value of highway fuel consumption. b. What are the specific values of the slope and y-intercept of the regression line? OA. The slope is 58.9 and the y-intercept is 0.007499. OB. The slope is -0.00749 and the y-intercept is 58.9. OC. The slope is 0.00749 and the y-intercept is 58.9. O D. The slope is 58.9 and the y-intercept is -0.00749. c. What is the predictor variable? OA. The predictor variable is highway fuel consumption, which is represented by y. OB. The predictor variable is weight, which is represented by x. OC. The predictor variable is weight, which is represented by y. OD. The predictor variable is highway fuel consumption, which is represented by x. (...) Car Data Weight Highway Fuel Consumption 2844 36 3109 38 41 33 42 31 40 2870 3095 2915 2985 2563 3009 2798 2468 2598 2558 3452 3514 Print 37 35 40 38 37 36 32 Done d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2997 lb? mi/gal. The best predicted value of highway fuel consumption of a car that weighs 2997 lb is (Round to one decimal place as needed.) - X
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