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| 1.
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In order to test the significance of the multiple regression model, we use: |
| | A) | the partial F test |
| | B) | t test |
| | C) | the overall F test |
| | D) | Durbin Watson test |
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| 2.
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In order to test the significance of a single independent variable, we use: |
| | A) | the partial F test |
| | B) | t test |
| | C) | the overall F test |
| | D) | Durbin Watson test |
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| 3.
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The general form of the quadratic multiple regression model is: |
| | A) | y = β1X1 + β2X2 + ∈ |
| | B) | y = β0 + β1X1 + β2X2 + ∈ |
| | C) | y = β0 + β1X + β2X2 + ∈ |
| | D) | y = β0 + β2X2 + ∈ |
| | E) | y = β0 + β1X1 + β2X22+ ∈ |
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| 4.
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Which is not an assumption of a multiple regression model? |
| | A) | Positive autocorrelation of error terms |
| | B) | Normality of error terms |
| | C) | Independence of error terms |
| | D) | Constant variation of error terms |
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| 5.
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The range of feasible values for the multiple coefficient of determination is from: |
| | A) | 0 to ∞ |
| | B) | -1 to 0 |
| | C) | -1 to 1 |
| | D) | 0 to 1 |
| | E) | -∞ to 0 |
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| 6.
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An acceptable residual plot exhibits |
| | A) | increasing error variance. |
| | B) | decreasing error variance. |
| | C) | constant error variance. |
| | D) | curved pattern. |
| | E) | an alternating pattern or a cyclical pattern. |
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| 7.
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Multicollinearity between independent variables is serious if the mean variance inflation factor (VIF) is |
| | A) | substantially less than l. |
| | B) | substantially greater than l. |
| | C) | between -1 and +1. |
| | D) | less than .5. |
| | E) | substantially less than zero. |
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| 8.
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In a multiple regression analysis, if the normal plot of the residuals ______________, then it can be concluded that the assumption of normality is not violated. |
| | A) | is a straight line |
| | B) | has the shape of a symmetric bell shaped curve |
| | C) | is greatly curved |
| | D) | Is left skewed |
| | E) | has the shape of a parabola that opens upward |
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| 9.
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Cook's distance measure is used in |
| | A) | determining if there is significant first order autocorrelation. |
| | B) | identifying influential observations in multiple regression analysis. |
| | C) | determining the significance of an independent variable. |
| | D) | determining if there is significant multicollinearity. |
| | E) | determining if the overall regression model is significant. |
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| 10.
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Given the regression model
Y = β0 + β1X1 + β2X2 + β3X12 + β4X22 + ∈,
if we wish to test the significance of the higher ordered terms (X12 and X22), we would use the |
| | A) | overall F test. |
| | B) | Durbin-Watson test. |
| | C) | partial F test. |
| | D) | t test. |
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| 11.
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In the quadratic regression model, μ = β0 + β1x + β2x2, β2 is called the: |
| | A) | Y-intercept of the parabola |
| | B) | Shift parameter of the parabola |
| | C) | Rate of curvature of the parabola |
| | D) | Linear function of the regression |
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| 12.
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For the same point estimate of the dependent variable and the same level of alpha, the confidence interval is always wider than the corresponding prediction interval. |
| | A) | True |
| | B) | False |
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| 13.
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An application of the multiple regression model generated the following results involving the F test of the overall regression model: p - value = .0012, R2 = .67, s = .076. Thus, the null hypothesis, which states that none of the independent variables are significantly related to the dependent variable, should be rejected, at the .05 level of significance. |
| | A) | True |
| | B) | False |
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| 14.
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For a multiple regression model, the computer output shows that the simple correlation coefficient between the dependent variable and one of the independent variables is .99. This result indicates that most likely the problem of multicollinearity exists in this model. |
| | A) | True |
| | B) | False |
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