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1 | |
In a linear regression equation of the form Y = a + bX, the intercept parameter a shows... |
| | A) | the amount that Y changes when X changes by one unit. |
| | B) | the amount that X changes when Y changes by one unit. |
| | C) | the value of Y when X is zero. |
| | D) | the value of X when Y is zero. |
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2 | |
The sample regression line... |
| | A) | is estimated by the population regression line. |
| | B) | is used to estimate the population regression line. |
| | C) | connects the sample data points. |
| | D) | shows the true relation between the dependent and independent variables. |
| | E) | none of the above |
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3 | |
In a linear regression equation Y = a + bX, the fitted or predicted value of Y is... |
| | A) | the value of X associated with a particular value of Y. |
| | B) | the value of Y associated with a particular value of X in the sample. |
| | C) | the value of X predicted by the regression equation. |
| | D) | the values of the parameters predicted by the estimators. |
| | E) | the value of Y obtained by substituting specific values of X into the sample regression equation. |
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4 | |
A parameter is said to be statistically significant if there is sufficient evidence that the |
| | A) | sample regression line is equal to the population regression. |
| | B) | parameter estimated from the sample equals the true value of the parameter. |
| | C) | true value of the parameter does not equal zero. |
| | D) | value of the t-ratio equals the critical value. |
| | E) | both c and d |
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5 | |
An estimator is unbiased if it produces... |
| | A) | a parameter from the sample that equals the true parameter. |
| | B) | estimates of a parameter that are close to the true parameter. |
| | C) | estimates of a parameter that are, on average, equal to the true parameter. |
| | D) | estimates of a parameter that are statistically significant. |
| | E) | both b and d |
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6 | |
If the t-statistic exceeds the critical value of t , then one can... |
| | A) | not reject the hypothesis that the true value of a parameter equals zero. |
| | B) | reject the hypothesis that the true value of a parameter equals zero. |
| | C) | accept the hypothesis that the estimated value of parameter equals the true value. |
| | D) | reject the hypothesis that the estimated value of the parameter exceeds the true value. |
| | E) | reject the hypothesis that the estimated value of the parameter equals the true value. |
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7 | |
To test whether the overall regression equation is statistically significant, one uses the... |
| | A) | t-statistic. |
| | B) | R2-statistic |
| | C) | F-statistic. |
| | D) | standard error statistic. |
| | E) | all of the above |
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8 | |
To test the hypothesis that a parameter equals zero, one uses the... |
| | A) | t-statistic. |
| | B) | R2-statistic |
| | C) | F-statistic. |
| | D) | standard error statistic. |
| | E) | none of the above |
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9 | |
In the nonlinear function Y = aXbZc, the parameter c measures |
| | A) | ΔY / ΔZ |
| | B) | the percent change in Y for a 1 percent change in Z. |
| | C) | the elasticity of Y with respect to Z. |
| | D) | both b and c |
| | E) | all of the above |
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10 | |
If the level of significance is 1%, the |
| | A) | probability of getting statistical significance when the true value of the parameter is zero is 1%. |
| | B) | level of confidence is 99%. |
| | C) | probability of not finding statistical significance when the true value of the parameter is zero is 99%. |
| | D) | both a and b |
| | E) | all of the above |
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11 | |
If the level of confidence is 95%, the |
| | A) | probability of finding a parameter estimate to be statistically significant when the true value of the parameter is zero is 5%. |
| | B) | probability that the parameter estimate is equal to the true value of the parameter is 95%. |
| | C) | R-square is equal to 0.9500. |
| | D) | both a and c |
| | E) | all of the above |
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12 | |
In a multiple regression model, the coefficients on the independent variables measure the... |
| | A) | percent of the variation in the dependent variable explained by a change in that independent variable, all other influences held constant. |
| | B) | change in that independent variable from a 1-unit change in the dependent variable, all other influences held constant. |
| | C) | change in the dependent variable from a 1-unit change in that independent variable, all other influences held constant. |
| | D) | none of the above |
| | E) | |
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13 | |
The quadratic equation, Y = a + bX + cX2 can be estimated using linear regression by estimating... |
| | A) | Y = a + ZX where Z = (b + c)2 |
| | B) | Y = a + bX + cZ where Z = X2 |
| | C) | Y = a + ZX where Z = (b + c) |
| | D) | Y = a + bX + ZX where Z = c2 |
| | E) | none of the above will work |
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14 | |
The next 6 questions refer to the following:
 <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0070601607/124365/chap5_15.gif','popWin', 'width=610,height=312,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (5.0K)</a>]]>
The parameter estimate of a indicates... |
| | A) | that when X is zero, Y is 102.54. |
| | B) | that when X is zero, Y is 412.18. |
| | C) | ΔY / Δa |
| | D) | ΔY / ΔX |
| | E) | both a and c |
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15 | |
The parameter estimate of b indicates that... |
| | A) | X increases by 0.1765 units when Y increases by one unit. |
| | B) | X increases by 0.6358 units when Y increases by one unit. |
| | C) | Y increases by 0.1765 units when X decreases by one unit. |
| | D) | Y increases by 0.6358 units when X increases by one unit. |
| | E) | Y increases by 3.60 units when X increases by one unit. |
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16 | |
What is the critical value of t at the 5% level of significance? |
| | A) | 2.131 |
| | B) | 1.771 |
| | C) | 2.160 |
| | D) | 2.145 |
| | E) | none of the above |
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17 | |
Which of the following statements is correct at the 5% level of significance? |
| | A) |  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0070601607/124365/chap5_18a.gif','popWin', 'width=314,height=89,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (1.0K)</a>]]> |
| | B) |  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0070601607/124365/chap5_18b.gif','popWin', 'width=317,height=91,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (1.0K)</a>]]> |
| | C) |  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0070601607/124365/chap5_18c.gif','popWin', 'width=308,height=92,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (1.0K)</a>]]> |
| | D) |  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0070601607/124365/chap5_18d.gif','popWin', 'width=305,height=91,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (1.0K)</a>]]> |
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18 | |
The value of the R2 statistic indicates that |
| | A) | 60.10% of the total variation in X is explained by the regression equation. |
| | B) | 60.10% of the total variation in Y is explained by the regression equation. |
| | C) | 0.6010% of the total variation in Y is explained by the regression equation. |
| | D) | 0.6010% of the total variation in X is explained by the regression equation. |
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19 | |
If X equals 35, what is the predicted value of Y? |
| | A) | 593.24 |
| | B) | 634.71 |
| | C) | 389.93 |
| | D) | 434.43 |
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