While investigating how a car's odometer reading (mileage) affects its sale price, a researcher collects data for the sale price of 36 used (preowned) 2019 Ford Fusions of the same trim level (package). Mileage is in thousands of km and Sale Price is in thousands of dollars.

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Mileage('000km)	Sale_Price($'000)
29.4	29.4
20.5	29.3
37.2	28.5
55.0	20.6
79.0	19.3
64.4	25.9
70.7	24.2
37.8	23.4
58.4	20.4
59.8	21.3
76.3	20.6
68.7	19.4
27.0	23.6
51.0	25.2
59.9	24.3
50.5	21.2
62.8	20.0
34.7	26.6
13.4	30.0
60.2	25.3
16.4	29.5
32.5	25.0
31.5	29.1
62.9	20.0
68.9	22.2
14.5	27.9
41.6	25.9
53.1	20.8
45.5	21.8
13.2	30.8
15.3	24.8
61.3	23.2
16.0	30.7
53.8	26.9
54.8	24.6
46.6	21.4

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Use software to generate a linear regression output for the data. Report numeric answers accurate to at least 4 decimal places or copy output results and paste directly into the boxes. 1. The dependent variable is Select an answer Sale Price Mileage . 2. The slope of the regression equation for the linear relationship = . This indicates that the relationship is Select an answer positive strong negative very weak weak . Thus, as Mileage decreases, Sale Price Select an answer decreases increases on average. 3. The y-intercept = . Considering these are used cars, does the y-intercept have a practical meaning for these data? Select an answer No, it does not. Yes, it does. . Why? Select an answer Because the slope can be negative. Because car mileage can't be zero. Because the slope can't be zero. Because the intercept can be zero. Because the intercept can be negative. 4. The correlation coefficient = . This indicates a Select an answer weak neutral strong very weak linear relationship. 5. The percentage of the variation in Sale Price that is explained by Mileage (to 2 decimal places) is %%. 6. Use the unrounded results from regression output to predict the sale price of one of these cars with a mileage of 37,510 km. Report your answer to the nearest dollar. Do not round intermediate results. Predicted Sale Price = $$ .