Reler to the accompanying table, which was obtained using data from homes sold.

Question

Reler to the accompanying table, 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). Ifonly one predictor () variable is used to predict the seling price, which single variable is best? Why? cack the icon to vew the vanous regression equations Select the best choice O A. The best singie predctor variable is LA because the associated regression equation has the lowest P-value of 0.000 and the lowest adjusted R2 of 0.599 of the equations with a P-value or 0.000 B. Th0 best single predictor variable is LP because the associated regression equation has e lowest P-value of 0 000 and th0 highest adjusted R2 of 0 987 C. The best single predictor variable is LOT because the associated regression equation has the highest P-value of 0 028 and the lowest ad usted R2 of 0 225 D. None or the single predictor variables can be used to predict the seing price Regression Equations R Adjusted Regression Equation Predictor (x) P Variables LP, LA, LOT 0000 0988 0987 y 8,237 0984 LP-5.4LA-87 LOT LP, LA LP. LOT LA, LOT LP LA LOT Value R2 0.000 0.988 .987 y 8,527-0.981 LP-5.1 LA 0.000 098 09878527-0.981 LP-5.1LA 0000 0 0987 y=10412-0943 LP+504 LOT 0000 0.809 0.801 y:119,647 + 95.7 LA+ 14,522 LOT 0.000 0.987 0.987 y 8,825 0.950 LP 0.000 0.607 0.599 y 141,725 98.8 LA 0.028 0241 0225 v317.343 15.837 LOT

Explanation / Answer

Option B is correct because it has highest R^2 Value of 0.987 which means 98.7% variation in y can be explained if this vatoble LP is used to predict y. Also , it has p value of 0.000 which means it is a significant variable

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