While investigating how a car's odometer reading (mileage) affects its sale price, a researcher collects data for the sale price of 33 used (preowned) 2020 Nissan Altimas of the same trim level (package). Mileage is in thousands of km and Sale Price is in thousands of dollars.

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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 weak very weak . Thus, as Mileage increases, Sale Price Select an answer increases decreases 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 Yes, it does. No, it does not. . Why? Select an answer Because car mileage can't be zero. Because the intercept can be negative. Because the slope can't be zero. Because the intercept can be zero. Because the slope can be negative. 4. The correlation coefficient = . This indicates a Select an answer very weak weak neutral strong 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 30,960 km. Report your answer to the nearest dollar. Do not round intermediate results. Predicted Sale Price = $$

Mileage('000km)	Sale_Price($'000)
32.5	27
58.7	26.4
62.9	25.8
30.8	23.2
61.4	23.1
59.8	21.3
14.2	27.7
74.6	21.7
37.2	28.4
42.1	22.9
29.3	24.2
39.9	25.3
68	19.4
51.2	22.2
58.4	23.5
12.7	28.8
7.8	25.3
37.5	24.5
6.3	29.7
71.4	20.1
38.1	28.3
63.9	21.9
24.7	24.8
8.8	30.3
71.3	23
44.1	27.9
78.8	21.4
67.5	21.6
14.1	26.7
64.6	24.7
12	26
39.1	28.4
36.8	26.4