Mileage('000km) | Sale Price(S'000) 46.9 23.4 65.2 25.6 43.1 22.8 36.2 24.7 63.9 25.9 70.7 24.2 77.4 22.4 39.6 23.3 65.0 21.7 6.2 28.7 34.3 28.8 46.5 26.7 57.1 23.5 76.4 23.6 41.4 22.2 63.4 19.8 60.7 22.1 38.5 24.5 24.7 26.6 22.6 24.0 56.1 24.7 45.8 27.7 40.1 25.2 50.4 27.1 69.4 24.2 41.5 23.0 28.7 28.2 59.1 25.4 26.7 24.6 55.0 26.8 52.7 23.8 72.6 21.9 27.8 27.3 48.3 22.3 5.8 28.7 20.2 30.1 67.5 25.6 16.0 26.6 74.5 19.8 66.1 22.5 73.3 23.0 14.9 29.7 12.5 26.1 9.6 31.3 42.4 27.0 66.3 21.6 21.4 26.1IIi'li'hile investigating how a car's odometer reading [mileageJI affects its sale price, a researcher collecls data for the sale price of 4? used lpreowned] 2019 Toyota Carnrys of the same trim level [package}l. Mileage is in thousands oflcm and Sale Price is in thousands of dollars. Download CS'U' le Use software to generate a linear regression output for the data. Report numeric answers accurate to at leost4 decimal places or copy output results and paste directly into the boxes. 1. The independent variable is ~/ i . 2. The slope of the regression equation for the linear relationship : -. This indicates that the relationship is ~ri . Thus, as Mileage increases, Sale Price '~" on average. 3. The y-intercept : -. Considering these are used cars, does the y intercept have a practical meaning Jfor these data? -"' I . 'u'u'hy? ~r' 4. The correlation coefcient = 413151 1' . This indicates a linear relations hip. 5. The percentage of the variation in Sale Price that is explained by Mileage [to 2 decimal places} is 9th. 5. Use the unrounded results From regression output to predict the sale price of one of these cars with a mileage of 32,341] km. Report your answer to the nearest dollar. [Jo not round intermediate results. Predicted Sale Price = 3