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| 1 | |
In a scatter diagram, the |
| | A) | dependent variable is scaled along the horizontal axis. |
| | B) | independent variable is scaled along the vertical axis. |
| | C) | graph shows the relationship between two variables. |
| | D) | probabilities are plotted. |
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| 2 | |
In correlation analysis, we do the following: |
| | A) | consider several independent variables. |
| | B) | study the strength of the association between two variables. |
| | C) | compare variances. |
| | D) | compare means. |
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| 3 | |
The sample coefficient of correlation |
| | A) | Has the same sign as the slope of the regression equation. |
| | B) | Can range from -1.00 up to 10.00 |
| | C) | Is also called Student's t. |
| | D) | Has n degrees of freedom. |
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| 4 | |
The coefficient of determination |
| | A) | Is the square root of the coefficient of correlation. |
| | B) | Can range from -1.00 up to 1.00. |
| | C) | Reports the percent of the variation in the dependent variable explained by the independent variable |
| | D) | Is based on the error mean square. |
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| 5 | |
Suppose we developed the following least squares regression equation:  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=jpg::::/sites/dl/free/0073401765/663702/C13_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"> (1.0K)</a>]]>What can we conclude? |
| | A) | The dependent variable increases 3.5 for an increase of 1 in X. |
| | B) | The equation crosses the Y-axis at 2.1. |
| | C) | If X = 5, then  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=jpg::::/sites/dl/free/0073401765/663702/C13_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>]]>is 14. |
| | D) | There is a significant positive relationship between the dependent and independent variables. |
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| 6 | |
The standard error of estimate is |
| | A) | Based on squared deviations from the regression line. |
| | B) | May assume negative values. |
| | C) | Is in squared units of the independent variable. |
| | D) | the regression mean square error. |
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| 7 | |
Which of the following is not a necessary condition for regression analysis? |
| | A) | The standard deviation of each of the conditional distributions must be the same. |
| | B) | The Y values are independent. |
| | C) | For each X value, there is a group of Y values and these Y values are normally distributed. |
| | D) | The slope of the regression line is positive (increasing). |
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| 8 | |
Which of the following is not based on squared deviations from the regression line? |
| | A) | The coefficient of correlation. |
| | B) | The coefficient of determination. |
| | C) | The standard deviation. |
| | D) | The standard error of estimate. |
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| 9 | |
In an ANOVA table for simple linear regression, the degrees of freedom for the regression mean square is |
| | A) | Equal to 1. |
| | B) | n - 1. |
| | C) | n - 2. |
| | D) | n |
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| 10 | |
The term SSR/SS total is also called the |
| | A) | Sum of squares due to regression. |
| | B) | Coefficient of determination. |
| | C) | Standard error of estimate. |
| | D) | Coefficient of correlation. |
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