1.
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The Y intercept (b0) represents the:
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| predicted value of Y when X = 0. |
| estimated change in average Y per unit change in X. |
| predicted value of Y. |
| variation around the line of regression. |
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2.
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The slope (b1) represents:
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| predicted value of Y when X = 0. |
| the estimated change in average Y per unit change in X. |
| predicted value of Y. |
| variation around the line of regression. |
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3.
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The least squares method (OLS) minimizes which of the following?
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| SSR |
| SSE |
| SST |
| All of the above. |
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4.
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The standard error of the estimate is a measure of:
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| total variation of the Y variable. |
| the variation around the regression line. |
| explained variation. |
| the variation of the X variable. |
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5.
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The coefficient of determination tells us:
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| that the coefficient of correlation is larger than one. |
| whether r has any significance. |
| that we should not partition the total variation. |
| the proportion of total variation that is explained. |
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6.
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The residuals represent:
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| the difference between the actual Y values and the mean of Y. |
| the difference between the actual Y values and the predicted Y values. |
| the square root of the slope. |
| the predicted value of Y for the average X value. |
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7.
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If the plot of residuals is fan-shaped, which assumption is violated?
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| Normality of error. |
| Homoscedasticity. |
| Independence of errors. |
| No assumptions are violated; the graph should resemble a fan. |
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8.
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The strength of the linear relationship between two numerical variables may be measured by the:
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| scatter diagram. |
| correlation coefficient. |
| slope. |
| Y intercept. |
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9.
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In a simple linear regression problem, r and b1:
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| may have opposite signs. |
| must have the same sign. |
| must have opposite signs. |
| are equal. |
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10.
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Assuming a linear relationship between X and Y, if the coefficient of correlation (r) equals -0.30:
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| there is no correlation. |
| the slope (b1) is negative. |
| variable X is larger than variable Y. |
| the variance of X is negative. |
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11.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, what is the estimated slope parameter for the candy bar price and sales data?
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| 161.386 |
| 0.784 |
| -3.810 |
| -48.193 |
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12.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, what is the percentage of the total variation in candy bar sales explained by the regression model?
|
| 100% |
| 88.54% |
| 78.39% |
| 48.19% |
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13.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, what is the standard error of the estimate, SYX, for the data?
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| 0.784 |
| 0.885 |
| 12.550 |
| 15.299 |
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14.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, what is the standard error of the regression slope estimate, Sb1?
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| 0.784 |
| 0.585 |
| 12.550 |
| 15.299 |
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15.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, to test that the regression coefficient, (1, is not equal to 0, what would be the limits of the rejection region for b1? Use ( = 0.05.
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| -48.193 ( 45.245 |
| -48.193 ( 35.117 |
| - 48.193 ( 2.776 |
| 0 ( 35.117 |
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16.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, if the price of the candy bar is set at $2, the estimated average sales will be:
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| 30 |
| 65 |
| 90 |
| 100 |
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17.
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TABLE 13-1
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: | City | Price ($) | Sales | | River Falls | 1.30 | 100 | | Hudson | 1.60 | 90 | | Ellsworth | 1.80 | 90 | | Prescott | 2.00 | 40 | | Rock Elm | 2.40 | 38 | | Stillwater | 2.90 | 32 | Referring to Table 13-1, if the price of the candy bar is set at $2, the predicted sales will be:
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| 30 |
| 55 |
| 90 |
| 100 |
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18.
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The width of the confidence interval estimate for the predicted value of Y is dependent on:
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| the standard error of the estimate. |
| the value of X for which the prediction is being made. |
| the sample size. |
| All of the above. |
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19.
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Interpret the value of SYX = 65 in a simple linear regression.
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| About 95% of the observed Y values fall within 65 of the least squares line. |
| About 95% of the observed Y values equal their corresponding predicted values. |
| About 95% of the observed Y values fall within 130 of the least squares line. |
| For every one unit increase in X, we expect Y to increase by an estimated amount of 65. |
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20.
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A 95% confidence interval for (1 is (15,30). Interpret the interval.
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| We are 95% confident that the mean value of Y will fall between 15 and 30 units. |
| We are 95% confident that the X value will increase by between 15 and 30 units for every one unit increase in Y. |
| We are 95% confident that average value of Y will increase by between 15 and 30 units for every one unit increase in X. |
| At the 5% level of significance, there is no evidence of a linear relationship between Y and X. |
