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| 1 | |
In a multiple regression equation there are |
| | A) | Two or more dependent variable. |
| | B) | Two or more independent variables. |
| | C) | Two or more intercept values. |
| | D) | Multiple coefficients of determination |
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| 2 | |
A dummy variable or indicator variable |
| | A) | May assume only a value of 0 or 1. |
| | B) | Is another term for the dependent variable. |
| | C) | Is found by  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=jpg::::/sites/dl/free/0073401765/663702/C14_img1.jpg','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (2.0K)</a>]]>. |
| | D) | Is equal to  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=jpg::::/sites/dl/free/0073401765/663705/c13_o_img2.jpg','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (0.0K)</a>]]> |
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| 3 | |
The multiple standard error of estimate is |
| | A) | Found by taking the square root of SSR/SS total. |
| | B) | Negative when one of the net regression coefficients is 0. |
| | C) | Based on the term  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=jpg::::/sites/dl/free/0073401765/663702/C14_img2.jpg','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (2.0K)</a>]]>. |
| | D) | The same as the variance of a normal distribution. |
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| 4 | |
In the ANOVA table, the value of k is the |
| | A) | Sum of squares total. |
| | B) | Total number of observations |
| | C) | Number of degrees of freedom. |
| | D) | Number of independent variables. |
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| 5 | |
A correlation matrix shows the following: |
| | A) | All simple coefficients of correlation. |
| | B) | All possible net regression coefficients. |
| | C) | Correlations that are positive. |
| | D) | The multiple regression equation. |
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| 6 | |
The following statement is true for a multiple regression equation: |
| | A) | There is only one dependent variable. |
| | B) | The R2 term must be at least .50. |
| | C) | All the regression coefficient must be between -1.00 and 1.00. |
| | D) | There is only one (Y — Ÿ). |
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| 7 | |
Multicollinearity occurs when the following condition exists: |
| | A) | The residuals are correlated. |
| | B) | Time is involved in the analysis |
| | C) | The independent variables are correlated. |
| | D) | The residuals are not constant for all Y' values. |
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| 8 | |
For a multiple regression analysis, the global hypothesis test determines |
| | A) | Which independent variables do not equal 0. |
| | B) | Whether any of independent variables differ from 0. |
| | C) | Whether any of the correlation coefficients differ from 0. |
| | D) | Whether the intercept differs from 0. |
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| 9 | |
In testing the significance of individual regression coefficients |
| | A) | The test statistic is the t distribution. |
| | B) | We test the independent variables in pairs. |
| | C) | We usually delete the variables where the null hypothesis is rejected. |
| | D) | Regression coefficients with a negative coefficient are deleted from the equation. |
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| 10 | |
A residual |
| | A) | Has the same degrees of freedom as the MSE term. |
| | B) | Cannot assume a negative value. |
| | C) | Is also called the correlation matrix. |
| | D) | Is the difference between the actual and the predicted value of the dependent variable. |
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