A real estate magazine reported the results of a regression analysis designed to

Question

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, y = beta_0 + beta_1 x. The results of the simple linear regression are provided below. y = 96,600 + 22.5x s = 6500 r^2 = -0.77 t = 6.1 (for testing beta_1) Interpret the estimate of beta_0, the y-intercept of the line. About 95% of the observed sale prices fall within $96,600 of the least squares line For every 1-sq ft. increase in GLA, we expect a property's sale price to increase $96,600. There is no practical interpretation, since a gross living area of 0 is a nonsensical value. All residential properties in Virginia will sell for at least $96,600.

Explanation / Answer

from above as intercept is value when x or gross living area is 0

option C and D is right; while option C is most righteous chice

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