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Q:
Michelle used a sample of 50 U.S. cities to estimate the relationship between crime, or annual property crimes per 100,000 persons, and income, or median income per capita. Her estimated regression equation was crime = 428 - .01 income. Assuming her model is statistically significant, if income decreases by $1,000, we would predict that crime will
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A. decrease by 1 |
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B. increase by 10 |
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C. decrease by 10 |
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D. increase by 100 |
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In the least squares equation, Y' = 12 + 25X the value of 25 indicates
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A. the y- intercept |
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B. for each unit increase in 'x', y' increases by 25 |
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C. for each unit increase in 'y', 'x' increases by 25 |
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D. the residual of 'x' factor |
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A test is conducted in eight cities to see if giving away free transit system maps will increase bus ridership. In a regression analysis, the dependent variable is the increase in bus ridership in thousands of persons from the start of the test until its conclusion. The independent variables are X1 = the number in thousands of free maps distributed and X2 = a binary equal to 1, if the city has free downtown parking and 0 otherwise. The estimated regression equation is Y = 1.32+.0345X1-1.45X2. If the Y value for city 3 is 7.3, X1 = 140, and X2 = 0 the residual for city 3 in thousands is:
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A. 6.34 |
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B. 1.15 |
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C. .57 |
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D. 2.01 |
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