Using 20 observations, the multiple regression model y = beta_0 + beta_1x_1 + be

ID: 3217663 • Letter: U

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Using 20 observations, the multiple regression model y = beta_0 + beta_1x_1 + beta_2x_2 + epsilon was estimated. Excel produced the following relevant results. a. At the 5% significance level, are the explanatory variables jointly significant? Yes, since the p-value of the appropriate test is more than 0.05. No, since the p-value of the appropriate test is less than 0.05. No, since the p-value of the appropriate test is more than 0.05. Yes, since the p-value of the appropriate test is less than 0.05. b. At the 5% significance level, is each explanatory variable individually significant? Yes, since both p-values of the appropriate test are less than 0.05. Yes, since both p-values of the appropriate test are more than 0.05. No, since both p-values of the appropriate test are not less than 0.05. No, since both p-values of the appropriate test are not more than 0.05. c. What is the likely problem with this model? Multicollinearity since the standard errors are biased. Multicollinearity since the explanatory variables are individually and jointly significant. Multicollinearity since the explanatory variables are individually significant but jointly Insignificant. Multicollinearity since the explanatory variables are individually insignificant but jointly significant.

Explanation / Answer

a) Yes. since the p value of the F-test is lessthan 0.05

Explanation: In anova table the value of P = 0.0000<0.05 .

Hence the model is good fit and significant

b) No.Since both the p values of the appropriate test are not less than 0.05 (>0.05)

Explanation: Here the p value of X1 is 0.3702>0.05 and the p value of X2 is 0.3567>0.05

therefore both the variables are insignificant

c) Multicolinearity since the the standard errors are bised

Explanation: Multicollinearity is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.

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Using 20 observations, the following regression output is obtained from estimati

Using 20 observations, the multiple regression model y produced the following re

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Using 20 observations, the multiple regression model y = beta_0 + beta_1x_1 + be

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