Multiple Regression call: 1m(formula Energy Bar Sprice - Energy_BarScalories + Energy_Bar Sprotein + Energy Bar$Fat) Residuals: Min 1Q Median 3Q Max -0.52712 -0.18270 -0.09316 0.23599 0.47918 Coefficients: (Intercept) 0.936 Estimate Std. Error t value Pr(>\t]) -0.062159 0.757488 -0.082 Energy BarScalories 0.002590 0.002916 0.888 Energy BarSProtein 0.048206 0.029663 1.625 0.132 Energy Bar$Fat 0.043968 0.048908 0.899 0.388 0.393 Residual standard error: 0.3165 on 11 degrees of freedom Multiple R-squared: 0.392, Adjusted R-squared: 0.2262 F-statistic: 2.364 on 3 and 11 DF, p-value: 0.127 a. Interpret each of the results presented in the output and provide an interpretation for each of them. b. Using a significance level of 0.05, determine whether the data support the hypothesis of a linear relationship between dependent and independent variables.
Multiple Regression call: 1m(formula Energy Bar Sprice - Energy_BarScalories + Energy_Bar Sprotein + Energy Bar$Fat) Residuals: Min 1Q Median 3Q Max -0.52712 -0.18270 -0.09316 0.23599 0.47918 Coefficients: (Intercept) 0.936 Estimate Std. Error t value Pr(>\t]) -0.062159 0.757488 -0.082 Energy BarScalories 0.002590 0.002916 0.888 Energy BarSProtein 0.048206 0.029663 1.625 0.132 Energy Bar$Fat 0.043968 0.048908 0.899 0.388 0.393 Residual standard error: 0.3165 on 11 degrees of freedom Multiple R-squared: 0.392, Adjusted R-squared: 0.2262 F-statistic: 2.364 on 3 and 11 DF, p-value: 0.127 a. Interpret each of the results presented in the output and provide an interpretation for each of them. b. Using a significance level of 0.05, determine whether the data support the hypothesis of a linear relationship between dependent and independent variables.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![Multiple Regression
Call:
1m (formula Energy Bar SPrice Energy BarScalories + Energy Bar $Protein +
Energy Bar$Fat)
Residuals:
Min
1Q Median
3Q
Max
-0.52712 -0.18270 -0.09316 0.23599 0.47918
Coefficients:
(Intercept)
Estimate Std. Error t value Pr(>\t])
-0.062159 0.757488 -0.082
Energy BarScalories 0.002590 0.002916
Energy BarSProtein 0.048206 0.029663
Energy Bar$Fat
0.888
1.625
0.043968 0.048908 0.899
0.936
0.393
0.132
0.388
Residual standard error: 0.3165 on 11 degrees of freedom
Multiple R-squared: 0.392, Adjusted R-squared: 0.2262
F-statistic: 2.364 on 3 and 11 DF, p-value: 0.127
a. Interpret each of the results presented in the output and provide an
interpretation for each of them.
b. Using a significance level of 0.05, determine whether the data support the
hypothesis of a linear relationship between dependent and independent
variables.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbe6167b7-ba28-42af-bfcb-148cd0e6c4e0%2Fa3a490b1-4cd8-4a6d-a8d6-a5d92f4a14df%2Fsmss35i_processed.png&w=3840&q=75)
Transcribed Image Text:Multiple Regression
Call:
1m (formula Energy Bar SPrice Energy BarScalories + Energy Bar $Protein +
Energy Bar$Fat)
Residuals:
Min
1Q Median
3Q
Max
-0.52712 -0.18270 -0.09316 0.23599 0.47918
Coefficients:
(Intercept)
Estimate Std. Error t value Pr(>\t])
-0.062159 0.757488 -0.082
Energy BarScalories 0.002590 0.002916
Energy BarSProtein 0.048206 0.029663
Energy Bar$Fat
0.888
1.625
0.043968 0.048908 0.899
0.936
0.393
0.132
0.388
Residual standard error: 0.3165 on 11 degrees of freedom
Multiple R-squared: 0.392, Adjusted R-squared: 0.2262
F-statistic: 2.364 on 3 and 11 DF, p-value: 0.127
a. Interpret each of the results presented in the output and provide an
interpretation for each of them.
b. Using a significance level of 0.05, determine whether the data support the
hypothesis of a linear relationship between dependent and independent
variables.
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