If the coefficient B₁ has a nonzero value, then it is helpful in predicting the value of the response variable. If B₁ = 0, it is not helpful in predicting the value of the response variable and can be eliminated from the regression equation. To test the claim that B₁ = 0 use the test statistic t = (b₁ - 0) /sp. Critical values or P-values can be found using the t distribution with n - (k+1) degrees of freedom, where k is the number of predictor (x) variables and n is the number of observations in the sample. The standard error sp, is often provided by software. For example, see the accompanying technology display, which shows that sp, = 0.070306916 (found in the column with the heading of "Std. Err." and the row corresponding to the first predictor variable of height). Use the technology display to test the claim that B₁ = 0. Also test the claim that B₂ = 0. What do the results imply about the regression equation? Technology Output Parameter estimates: Parameter Estimate Std. Err. - 147.38571 12.118648 0.75317073 0.070306916 1.0419968 0.030863713 Intercept Height Waist Alternative DF+ T-Stat #0 150 12.161894 #0 150 #0 150 10.712612 33.761226 P-value - X <0.0001 <0.0001 <0.0001 ression coefficient

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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78

If the coefficient B₁ has a nonzero value, then it is helpful in predicting the value of the response variable. If B₁ = 0, it is not helpful in
predicting the value of the response variable and can be eliminated from the regression equation. To test the claim that B₁ = 0 use the test
statistic t = (b₁ - 0) /sp. Critical values or P-values can be found using the t distribution with n - (k+1) degrees of freedom, where k is the
number of predictor (x) variables and n is the number of observations in the sample. The standard error sp, is often provided by software.
For example, see the accompanying technology display, which shows that sp, = 0.070306916 (found in the column with the heading of "Std.
Err." and the row corresponding to the first predictor variable of height). Use the technology display to test the claim that B₁ = 0. Also test the
claim that B₂ = 0. What do the results imply about the regression equation?
Technology Output
Parameter estimates:
Intercept
Parameter Estimate Std. Err.
- 147.38571 12.118648
0.75317073 0.070306916
1.0419968 0.030863713
Height
Waist
Alternative
DF+ T-Stat
#0 150 12.161894
#0 150
#0 150
10.712612
33.761226
P-value
- X
<0.0001
<0.0001
<0.0001
ression coefficient
Transcribed Image Text:If the coefficient B₁ has a nonzero value, then it is helpful in predicting the value of the response variable. If B₁ = 0, it is not helpful in predicting the value of the response variable and can be eliminated from the regression equation. To test the claim that B₁ = 0 use the test statistic t = (b₁ - 0) /sp. Critical values or P-values can be found using the t distribution with n - (k+1) degrees of freedom, where k is the number of predictor (x) variables and n is the number of observations in the sample. The standard error sp, is often provided by software. For example, see the accompanying technology display, which shows that sp, = 0.070306916 (found in the column with the heading of "Std. Err." and the row corresponding to the first predictor variable of height). Use the technology display to test the claim that B₁ = 0. Also test the claim that B₂ = 0. What do the results imply about the regression equation? Technology Output Parameter estimates: Intercept Parameter Estimate Std. Err. - 147.38571 12.118648 0.75317073 0.070306916 1.0419968 0.030863713 Height Waist Alternative DF+ T-Stat #0 150 12.161894 #0 150 #0 150 10.712612 33.761226 P-value - X <0.0001 <0.0001 <0.0001 ression coefficient
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If the coefficient B₁ has a nonzero value, then it is helpful in predicting the value of the response variable. If B₁ = 0, it is not helpful in
predicting the value of the response variable and can be eliminated from the regression equation. To test the claim that B₁ = 0 use the test
statistic t= (b₁-0) /sp. Critical values or P-values can be found using the t distribution with n - (k+ 1) degrees of freedom, where k is the
number of predictor (x) variables and n is the number of observations in the sample. The standard error sp, is often provided by software.
For example, see the accompanying technology display, which shows that sp, = 0.070306916 (found in the column with the heading of "Std.
Err." and the row corresponding to the first predictor variable of height). Use the technology display to test the claim that B₁ = 0. Also test the
claim that B₂ = 0. What do the results imply about the regression equation?
Click the icon to view the technology output.
Test the claim that B₁ = 0.
For Ho: B₁ = 0, the test statistic is t= 10.328 and the P-value is 0, so
b₁ = 0.725 should be kept.
(Round to three decimal places as needed.)
reject Ho and conclude that the regression coefficient
Transcribed Image Text:If the coefficient B₁ has a nonzero value, then it is helpful in predicting the value of the response variable. If B₁ = 0, it is not helpful in predicting the value of the response variable and can be eliminated from the regression equation. To test the claim that B₁ = 0 use the test statistic t= (b₁-0) /sp. Critical values or P-values can be found using the t distribution with n - (k+ 1) degrees of freedom, where k is the number of predictor (x) variables and n is the number of observations in the sample. The standard error sp, is often provided by software. For example, see the accompanying technology display, which shows that sp, = 0.070306916 (found in the column with the heading of "Std. Err." and the row corresponding to the first predictor variable of height). Use the technology display to test the claim that B₁ = 0. Also test the claim that B₂ = 0. What do the results imply about the regression equation? Click the icon to view the technology output. Test the claim that B₁ = 0. For Ho: B₁ = 0, the test statistic is t= 10.328 and the P-value is 0, so b₁ = 0.725 should be kept. (Round to three decimal places as needed.) reject Ho and conclude that the regression coefficient
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