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c. Choose the correct choice below. ○ A. The predictor is weight. The response is time spent in the gym. Clients with lower weights are more likely to have spent a large amount of time working out. ○ B. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are less likely to have lost weight. OC. The predictor is weight. The response is time spent in the gym. Clients with lower weights are less likely to have spent a large amount of time working out. ○ D. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are more likely to have lost weight. Statistical Outputs Simple linear regression results: Dependent Variable: Armspan Independent Variable: Height Armspan=-10.0645527 +2.724802 Height Sample size: 15 R(correlation coefficient) = 0.9074 R-sq=0.82339527 Estimate of error of standard deviation: 3.797606 Intercept Coefficients 10.064553 X Variable 2.724802 LinReg y= a + bx a = -10.06455273 b=2.724801813 2 r² = 0.823395273 r = 0.907411303

c. Choose the correct choice below. ○ A. The predictor is weight. The response is time spent in the gym. Clients with lower weights are more likely to have spent a large amount of time working out. ○ B. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are less likely to have lost weight. OC. The predictor is weight. The response is time spent in the gym. Clients with lower weights are less likely to have spent a large amount of time working out. ○ D. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are more likely to have lost weight. Statistical Outputs Simple linear regression results: Dependent Variable: Armspan Independent Variable: Height Armspan=-10.0645527 +2.724802 Height Sample size: 15 R(correlation coefficient) = 0.9074 R-sq=0.82339527 Estimate of error of standard deviation: 3.797606 Intercept Coefficients 10.064553 X Variable 2.724802 LinReg y= a + bx a = -10.06455273 b=2.724801813 2 r² = 0.823395273 r = 0.907411303

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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c. Choose the correct choice below. ○ A. The predictor is weight. The response is time spent in the gym. Clients with lower weights are more likely to have spent a large amount of time working out. ○ B. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are less likely to have lost weight. OC. The predictor is weight. The response is time spent in the gym. Clients with lower weights are less likely to have spent a large amount of time working out. ○ D. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are more likely to have lost weight.
Transcribed Image Text:c. Choose the correct choice below. ○ A. The predictor is weight. The response is time spent in the gym. Clients with lower weights are more likely to have spent a large amount of time working out. ○ B. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are less likely to have lost weight. OC. The predictor is weight. The response is time spent in the gym. Clients with lower weights are less likely to have spent a large amount of time working out. ○ D. The predictor is time spent in the gym. The response is weight. Clients who have spent more time working out are more likely to have lost weight.
Statistical Outputs Simple linear regression results: Dependent Variable: Armspan Independent Variable: Height Armspan=-10.0645527 +2.724802 Height Sample size: 15 R(correlation coefficient) = 0.9074 R-sq=0.82339527 Estimate of error of standard deviation: 3.797606 Intercept Coefficients 10.064553 X Variable 2.724802 LinReg y= a + bx a = -10.06455273 b=2.724801813 2 r² = 0.823395273 r = 0.907411303
Transcribed Image Text:Statistical Outputs Simple linear regression results: Dependent Variable: Armspan Independent Variable: Height Armspan=-10.0645527 +2.724802 Height Sample size: 15 R(correlation coefficient) = 0.9074 R-sq=0.82339527 Estimate of error of standard deviation: 3.797606 Intercept Coefficients 10.064553 X Variable 2.724802 LinReg y= a + bx a = -10.06455273 b=2.724801813 2 r² = 0.823395273 r = 0.907411303
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