5. To evaluate the assumption of linearity, a multiple regression analysis shoul

Question

5.            To evaluate the assumption of linearity, a multiple regression analysis should include ___________.

A.            A calculation of variance inflation factors

B.            Hypothesis tests of individual regression coefficients

C.            Scatter diagrams of the dependent variable plotted as a function of each independent variable

D.            An ANOVA table

Your Answer: Question 5 Answer SelectABCDE

6.

6.            In multiple regression analysis, residual analysis is used to test the requirement that ___________.

A.            The variation in the residuals is the same for all predicted values of Y

B.            The independent variables are the direct cause of the dependent variable

C.            The number of independent variables included in the analysis is correct

D.            The prediction error is minimized

7.            In an ANOVA table, for a multiple regression analysis, the variation of the dependent variable explained by the variation of the independent variables is represented by ___________.

A.            The regression sum of squares

B.            The total sum of squares

C.            The residual mean square

D.            The p-value

8.            If the coefficient of multiple determination is 0.81, what percent of variation is not explained?

A.            19%

B.            90%

C.            66%

D.            81%

9.

9.            In multiple regression analysis, before testing the significance of the individual regression coefficients, _______________.

A.            The intercept must equal 0

B.            The multiple standard error of the estimate must be less than the error mean square

C.            The null hypothesis that all regression coefficients equal zero must NOT be rejected

D.            The null hypothesis that all regression coefficients equal zero must be rejected

10.          The variance inflation factor can be used to reduce multicollinearity by _________.

A.            Eliminating variables for a multiple regression model

B.            Decreasing homoscedasticity

C.            Evaluating the distribution of residuals

D.            Testing the null hypothesis that all regression coefficients equal zero

Explanation / Answer

7)

In an ANOVA table, for a multiple regression analysis, the variation of the dependent variable explained by the variation of the independent variables is represented by ___________.

A.            The regression sum of squares

8) If the coefficient of multiple determination is 0.81, percent of variation is not explained is19%

9)In multiple regression analysis, before testing the significance of the individual regression coefficients ,The null hypothesis that all regression coefficients equal zero must be rejected

10)

The variance inflation factor can be used to reduce multicollinearity by _________.

A.            Eliminating variables for a multiple regression model

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