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A statistics student is studying if there is a relationship between the price of a used car and the number of miles it had been driven. She collects data for 20 cars of the same model with different mileage and determines each car’s price using a used car website. The analysis is given in the computer output.


Predictor Coef SE Coef t-ratio p

Constant 24157. 2 2164. 1 2. 965 0. 046

Mileage -0. 181 0. 024 5. 377 0. 000

S = 3860. 7 R-Sq = 68. 0% R-Sq(Adj) = 67. 5%

Using the computer output, what is the predicted price for a car with 10,000 miles on the odometer?


$22,347. 20

$23,917. 20

$24,157. 20

$25,967. 20

Sagot :

Lanuelidn

Based on the computer output, the predicted price for a car with 10,000 miles is $22,347.20.

What is a least-squares regression?

A least-squares regression line can be defined as a standard technique in regression analysis and statistics that is typically used to make the vertical distance obtained from the data points running to the regression line to become very minimal or as small as possible.

In statistics, the sum of the residuals for a least-squares regression line is always zero (0) because it determines whether or not a line of regression is a good fit or match for the given data.

Based on the computer output, the predicted price for a car that has covered a distance of 10,000 miles on the odometer is $22,347.20.

Read more on least-squares regression here: https://brainly.com/question/26059078

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