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| 1 | |
In a linear regression equation of the form Y = a + bX, the slope parameter b shows… |
| | A) | ΔY / Δb. |
| | B) | ΔX / Δb. |
| | C) | ΔY / ΔX. |
| | D) | ΔX / ΔY. |
| | E) | none of the above |
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| 2 | |
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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| 3 | |
The sample regression line… |
| | A) | is the same as 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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| 4 | |
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 X predicted by the regression equation. |
| | C) | the values of the parameters predicted by the estimators. |
| | D) | the value of Y obtained by substituting specific values of X into the estimated sample regression equation. |
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| 5 | |
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 b and c |
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| 6 | |
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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| 7 | |
If the t-statistic exceeds the critical value of t , then one can… |
| | A) | not reject the hypothesis that the true value of the parameter equals zero. |
| | B) | reject the hypothesis that the true value of the parameter equals zero. |
| | C) | accept the hypothesis that the estimated value of the 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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| 8 | |
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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| 9 | |
To test the hypothesis that a particular 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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| 10 | |
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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| 11 | |
If the p-value is 0.01 for the parameter estimate for b, the |
| | A) | probability of finding statistical significance when the true value of b is zero is exactly 1%. |
| | B) | probability of finding significance for the estimate of b when none exists is exactly 0.01%. |
| | C) | level of confidence is 99.99%. |
| | D) | probability that the parameter estimate equals the true value of b is 1%. |
| | E) | probability that the parameter estimate equals the true value of b is 0.01%. |
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| 12 | |
If the level significance for testing is specified to be 5%, then the parameter estimate for b is statistically significant if its p-value is… |
| | A) | 0.048. |
| | B) | 0.48. |
| | C) | greater than 0.05. |
| | D) | equal to the true value of b. |
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| 13 | |
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 the 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 |
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| 14 | |
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 + bZ 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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| 15 | |
In the regression above, 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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| 16 | |
In the regression above, 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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| 17 | |
In the regression above, 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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| 18 | |
In the regression above, which of the following statements is correct at the 5% level of significance? |
| | A) | Both â and b are statistically significant. |
| | B) | Neither â nor b is statistically significant. |
| | C) | â is statistically significant, but b is not. |
| | D) | b is statistically significant, but â is not. |
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| 19 | |
In the regression above, 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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| 20 | |
In the regression above, 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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