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The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 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 27 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? E Click the icon to view the Minitab display. - X Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4 -0.00535 Weight Predictor Constant Weight -0.0053502 0.0007856 Coef SE Coef 50.383 2.732 17.75 0.000 -7.55 0.000 s=2.17876 R-Sq = 65.1% R- Sq(adj) = 62.2% Predicted Values for New Observations New Obs SE Fit 0.546 Fit 95% CI 95% PI 26.307 (25.302, 27.312) (21.715, 30.899) Values of Predictors for New Observations New Obs Weight 4500

The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 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 27 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? E Click the icon to view the Minitab display. - X Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4 -0.00535 Weight Predictor Constant Weight -0.0053502 0.0007856 Coef SE Coef 50.383 2.732 17.75 0.000 -7.55 0.000 s=2.17876 R-Sq = 65.1% R- Sq(adj) = 62.2% Predicted Values for New Observations New Obs SE Fit 0.546 Fit 95% CI 95% PI 26.307 (25.302, 27.312) (21.715, 30.899) Values of Predictors for New Observations New Obs Weight 4500

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 15PPS
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The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 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 27 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. Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4- 0.00535 Weight %3D Predictor Сoef SE Coef T Constant 50.383 2.732 17.75 0.000 Weight - 0.0053502 0.0007856 -7.55 0.000 S= 2.17876 R-Sq = 65.1% R- Sq(adj) = 62.2% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 26.307 0.546 (25.302, 27.312) (21.715, 30.899) Values of Predictors for New Observations New Obs Weight 1 4500
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 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 27 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. Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4- 0.00535 Weight %3D Predictor Сoef SE Coef T Constant 50.383 2.732 17.75 0.000 Weight - 0.0053502 0.0007856 -7.55 0.000 S= 2.17876 R-Sq = 65.1% R- Sq(adj) = 62.2% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 26.307 0.546 (25.302, 27.312) (21.715, 30.899) Values of Predictors for New Observations New Obs Weight 1 4500
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