Refer to the table below which was obtained using data from

Refer to the table below, which was obtained using data from homes sold- The response (y) variable is selling price (in dollars). The predictor (x) variables are LP (list price in dollars), LA (living area of the home in square feet), and LOT (lot size in acres). Which regression equation is best for predicting the selling price? Why? Click the icon to view the various regression equations. Which regression equation is best for predicting the selling price? A. The best equation is y = 134,695 + 104.0 LA because it has the lowest P-value of 0.000 and the lowest adjusted R^2 of 0.565 of the equations with a P-value of 0.000. B. The best equation is y = 6,910 + 0.995 LP - 6.4 LA -253 LOT because it has the highest R^2 of 0.990, while also using the most number of predictor variables. C. The best equation is y = 322,037+16,239 LOT because it has the highest P-value of 0.040 and the lowest adjusted R^2 of 0.170, while also using the fewest number of predictor variables. D. The best equation is y = 6,975 + 0.956 LP because ft has the lowest P-value of 0.000 and the highest adjusted R^2 of 0-988, while also using the fewest number of predictor variables.

Solution

We want these on a regression model:

1. Low P value. [the first 6 have the same]
2. High R^2 value. [OPTIONS 1,2,3,5]
3. High adjusted R^2. [still a tie for them]
4. Least number of predictor variables. [5TH OPTION]

Thus,

OPTION D: The best equation is y^ = 6975 + 0.956LP... [ANSWER]