A real estate magazine reported the results of a regression analysis designed to predict the price (y), measured in dollars, of residential properties recently sold in a northern Virginia subdivision. One independent variable used to predict sale price is GLA, gross living area (x), measured in square feet. Data for 157 properties were used to fit the model, = [Math Processing Error]

The results of the simple linear regression are provided below. = [Math Processing Error]

Interpret the estimate of [Math Processing Error], the y-intercept of the line.

Question

Kenley

Mathematics

11 January, 10:47

A real estate magazine reported the results of a regression analysis designed to predict the price (y), measured in dollars, of residential properties recently sold in a northern Virginia subdivision. One independent variable used to predict sale price is GLA, gross living area (x), measured in square feet. Data for 157 properties were used to fit the model, = [Math Processing Error]

The results of the simple linear regression are provided below. = [Math Processing Error]

Interpret the estimate of [Math Processing Error], the y-intercept of the line.

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Answers (1)

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Albert Potter · Answer 1

let take the method ^y = 22.5x + 96,600

Step-by-step explanation:

All residential properties in Virginia will sell for at least $96,600.

For each 1-sq ft rise in GLA, a property's sale price is expected to go above $96,600.

Sale prices as observed would fall within$96,600 of the least squares line., as about 95% is discovered.

There is no value given, since a gross area of zero a not a constant value