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The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 33 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 33 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.6-0.00504 Weight Predictor SE Coef P Coef 50.557 2.829 0.000 Weight -0.0050407 0.0007681 -7.53 0.000 T 17.78 Constant S=2.12761 R-Sq=64.3% Predicted Values for New Observations New Obs 1 Fit 27.874 R-Sq(adj) = 61.9% SE Fit 0.471 Weight 4500 95% CI (26.869, 28.879) Values of Predictors for New Observations New Obs 1 95% PI (23.279, 32.469) X

The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 33 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 33 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.6-0.00504 Weight Predictor SE Coef P Coef 50.557 2.829 0.000 Weight -0.0050407 0.0007681 -7.53 0.000 T 17.78 Constant S=2.12761 R-Sq=64.3% Predicted Values for New Observations New Obs 1 Fit 27.874 R-Sq(adj) = 61.9% SE Fit 0.471 Weight 4500 95% CI (26.869, 28.879) Values of Predictors for New Observations New Obs 1 95% PI (23.279, 32.469) X

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
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The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 33 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 33 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.6 -0.00504 Weight Predictor Coef 50.557 Weight -0.0050407 0.0007681 Constant S=2.12761 R-Sq = 64.3% New Obs 1 SE Coef 2.829 Predicted Values for New Observations Fit 27.874 Weight 4500 SE Fit 0.471 R-Sq(adj) = 61.9% T 17.78 P 0.000 - 7.53 0.000 Values of Predictors for New Observations New Obs 1 95% CI (26.869, 28.879) 95% PI (23.279, 32.469) X
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 33 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 33 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.6 -0.00504 Weight Predictor Coef 50.557 Weight -0.0050407 0.0007681 Constant S=2.12761 R-Sq = 64.3% New Obs 1 SE Coef 2.829 Predicted Values for New Observations Fit 27.874 Weight 4500 SE Fit 0.471 R-Sq(adj) = 61.9% T 17.78 P 0.000 - 7.53 0.000 Values of Predictors for New Observations New Obs 1 95% CI (26.869, 28.879) 95% PI (23.279, 32.469) X
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