Question

The following model was fitted to a sample of 25 students using data obtained at the end of their freshman year in college. The aim was to explain students' weight gains: $$ y=\beta_0+\beta_1 x_1+\beta_2 x_2+\beta_3 x_3+\varepsilon $$ where $y=$ weight gained, in pounds, during freshman year $x_1=$ average number of meals eaten per week $x_2=$ average number of hours of exercise per week $x_3=$ average number of beers consumed per week The least squares estimates of the regression parameters were as follows: $$ b_0=7.35 \quad b_1=0.653 \quad b_2=-1.345 \quad b_3=0.613 $$ The estimated standard errors were as follows: $$ s_{b_1}=0.189 \quad s_{b_2}=0.565 \quad s_{b_3}=0.243 $$ The regression sum of squares and error sum of squares were found to be as follows: $$ S S R=79.2 \text { and } S S E=45.9 $$ a. Test the null hypothesis: $$ H_0: \beta_1=\beta_2=\beta_3=0 $$ b. Set out the analysis of variance table.

   The following model was fitted to a sample of 25 students using data obtained at the end of their freshman year in college. The aim was to explain students' weight gains:
$$
y=\beta_0+\beta_1 x_1+\beta_2 x_2+\beta_3 x_3+\varepsilon
$$
where
$y=$ weight gained, in pounds, during freshman year
$x_1=$ average number of meals eaten per week
$x_2=$ average number of hours of exercise per week
$x_3=$ average number of beers consumed per week
The least squares estimates of the regression parameters were as follows:
$$
b_0=7.35 \quad b_1=0.653 \quad b_2=-1.345 \quad b_3=0.613
$$
The estimated standard errors were as follows:
$$
s_{b_1}=0.189 \quad s_{b_2}=0.565 \quad s_{b_3}=0.243
$$
The regression sum of squares and error sum of squares were found to be as follows:
$$
S S R=79.2 \text { and } S S E=45.9
$$
a. Test the null hypothesis:
$$
H_0: \beta_1=\beta_2=\beta_3=0
$$
b. Set out the analysis of variance table.
Show more…
Statistics for Business and Economics: Global Edition
Statistics for Business and Economics: Global Edition
Newbold P., Carlson… 8th Edition
Chapter 12, Problem 39 ↓

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Step 1

- To test this hypothesis, we use the F-test. The F-statistic is calculated using the formula: \[ F = \frac{MSR}{MSE} \] where \( MSR \) (Mean Square Regression) is the regression mean square, and \( MSE \) (Mean Square Error) is the error  Show more…

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The following model was fitted to a sample of 25 students using data obtained at the end of their freshman year in college. The aim was to explain students' weight gains: $$ y=\beta_0+\beta_1 x_1+\beta_2 x_2+\beta_3 x_3+\varepsilon $$ where $y=$ weight gained, in pounds, during freshman year $x_1=$ average number of meals eaten per week $x_2=$ average number of hours of exercise per week $x_3=$ average number of beers consumed per week The least squares estimates of the regression parameters were as follows: $$ b_0=7.35 \quad b_1=0.653 \quad b_2=-1.345 \quad b_3=0.613 $$ The estimated standard errors were as follows: $$ s_{b_1}=0.189 \quad s_{b_2}=0.565 \quad s_{b_3}=0.243 $$ The regression sum of squares and error sum of squares were found to be as follows: $$ S S R=79.2 \text { and } S S E=45.9 $$ a. Test the null hypothesis: $$ H_0: \beta_1=\beta_2=\beta_3=0 $$ b. Set out the analysis of variance table.
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