Given the two models below , what can you conclude about factors that predict a

Question

Given the two models below , what can you conclude about factors that predict a home’s sale price? In other words, is ‘square feet’ or ‘number of days on the market’ better for explaining the variation in sales prices among homes? If you had access to the full dataset, what other variables would you want to test for their explanatory relationship to a home’s sales price? (3 points)

reg sold price approx sgft Source I 3849 F( 1, 3847)=9700.39 -0.0000 - 0.7160 Adj R-squared-0.7160 df MS Number of obs Model 6.5211e+14 1 6.5211e+14 Residual 2.5861e+14 3847 6.7225e+10 Prob > F R-squared Total 9.1072e+14 3848 2.3667e+11 Root MSE -2.6e+05 sold_price l Coef. Std. Err [95% Conf. Interval] approx_sqft 287.304 2.917071 98.49 0.000 281.5848 293.0231 -299686 cons I-318043.9 9363.475 -33.97 0.000 -336401.7

Explanation / Answer

R2 for the model with "square feet" independent variable = 0.7160

R2 for the model with "days on market" independent variable = 0.0780

So we can see that by square feet, 71.6% of the variation in prices can be explained whereas by using days on market, only 7.8% of the variation can be explained. Hence,

"Square feet" is a better predictor of selling price as compared to "number of days on market".

There are multiple variables which can affect the sales price of houses. If i had access to the full dataset, some variables which i might have included in the analysis are:

--> Number of bedrooms

--> Number of bathrooms

--> Number of years the house was initially build

--> Location

Related Questions

Navigate

• Browse (All)
• Browse G
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.