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| 1 | |
The residual is the difference between the observed value of the dependent variable and the predicted value of the dependent variable. |
| | A) | True |
| | B) | False |
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| 2 | |
The experimental region is the range of the previously observed values of the dependent variable. |
| | A) | True |
| | B) | False |
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| 3 | |
The simple coefficient of determination is the proportion of total variation explained by the regression line. |
| | A) | True |
| | B) | False |
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| 4 | |
When there is positive autocorrelation, over time, negative error terms are followed by positive error terms and positive error terms are followed by negative error terms. |
| | A) | True |
| | B) | False |
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| 5 | |
The coefficient of determination not only indicates the strength of the relationship between independent and dependent variable in a simple linear regression model but also shows whether the relationship is positive or negative. |
| | A) | True |
| | B) | False |
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| 6 | |
The point estimate of the variance of the error term in a regression model is |
| | A) | SSE |
| | B) | b0 |
| | C) | MSE |
| | D) | b1 |
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| 7 | |
All of the following are assumptions of the error terms in the simple linear regression model except |
| | A) | normality. |
| | B) | error terms with a mean of zero. |
| | C) | constant variance. |
| | D) | variance of one. |
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| 8 | |
The _______ measures the strength of the linear relationship between the dependent variable and the independent variable. |
| | A) | simple correlation coefficient |
| | B) | distance value |
| | C) | y-intercept |
| | D) | normal plot |
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| 9 | |
For the same set of observations on a specified dependent variable, two different independent variables were used to develop two simple linear regression models. The results are summarized as follows:
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Based on the above results, we can conclude that: |
| | A) | A prediction based on Model I is likely better than a prediction based on Model II. |
| | B) | A prediction based on Model II is likely better than a prediction based on Model I. |
| | C) | The SSE for Model II is smaller than the SSE for Model I. |
| | D) | The total variation is different for Model I and Model II. |
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| 10 | |
When the constant variance assumption holds, a plot of the residuals versus x |
| | A) | fans out. |
| | B) | funnels in. |
| | C) | fans out, but then funnels in. |
| | D) | forms a horizontal band pattern. |
| | E) | forms a normal distribution. |
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| 11 | |
The following results were obtained from a simple regression analysis:Y = 37.2895 - (1.2024)X
r2 = .6744
s2 = .2934
For each unit change in X (independent variable), the estimated change in the average value of Y (dependent variable) is equal to: |
| | A) | -1.2024 |
| | B) | .6774 |
| | C) | 37.2895 |
| | D) | .2934 |
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| 12 | |
Which of the following is not a violation of the independence assumption? |
| | A) | Negative autocorrelation |
| | B) | A pattern of cyclical error terms over time |
| | C) | Positive autocorrelation |
| | D) | A pattern of alternating error terms over time |
| | E) | A random pattern of error terms over time |
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| 13 | |
The dependent variable is the variable that is being described, predicted, or controlled. |
| | A) | True |
| | B) | False |
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| 14 | |
The simple linear regression model minimizes |
| | A) | the explained variation. |
| | B) | the unexplained variation. |
| | C) | total variation. |
| | D) | SSyy. |
| | E) | SSxx. |
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| 15 | |
In the regression model of 37.2895 - (1.2024)X, the slope is |
| | A) | equal to zero. |
| | B) | negative. |
| | C) | positive. |
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