Asked by Kelsee Katsanes on May 31, 2024

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In a multiple regression model involving 50 observations, the following estimated regression equation was obtained: ​ In a multiple regression model involving 50 observations, the following estimated regression equation was obtained: ​   = 20 + 5x<sub>1</sub> - 4x<sub>2</sub> + 8x<sub>3</sub> + 8x<sub>4</sub> ​ For this model, SSR = 700 and SSE = 100.At the 5% level, A)  there is no evidence that the model is significant. B)  it can be concluded that the model is significant. C)  the conclusion is that the slope of x<sub>1</sub> is significant. D)  there is evidence that the slope of x<sub>2</sub> is significant. = 20 + 5x1 - 4x2 + 8x3 + 8x4

For this model, SSR = 700 and SSE = 100.At the 5% level,

A) there is no evidence that the model is significant.
B) it can be concluded that the model is significant.
C) the conclusion is that the slope of x1 is significant.
D) there is evidence that the slope of x2 is significant.

Estimated Regression Equation

An equation derived from regression analysis used to predict the dependent variable based on one or more independent variables.

SSR

Sum of Squares due to Regression, a measure used in statistical analysis to determine the explanatory power of a regression model.

SSE

Sum of Squared Errors, a measure of the discrepancy between the data and an estimation model.

  • Discriminate among the diverse tests (t-test, F-test, chi-square test) utilized in the assessment of regression models.
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AJ
Amelia JOHNSONMay 31, 2024
Final Answer :
B
Explanation :
To test if the model is significant, we perform an F-test with the null hypothesis being that all the regression coefficients in the model are zero. The F-statistic is calculated as (SSR/4)/(SSE/45) = 35. Since the p-value associated with this F-statistic is less than 0.05, we can conclude that the model is significant. Option B is correct. Option A is incorrect because the model is significant. Option C and D are incorrect because we cannot determine the significance of individual coefficients without performing t-tests or looking at the confidence intervals.