Using this data, develop a multiple regression model to predict selling price ba
ID: 3201178 • Letter: U
Question
Using this data, develop a multiple regression model to predict selling price based on the square footage, number of bedroom and age. Please write down the regression equation. Use this to predict the selling price of a 9-year-old, 3.030 square foot house with 4 bedrooms.
selling price square footage Bedrooms Age 263,000 1,545 2 20 267,500 1,812 3 28 279,000 1,940 3 36 287,500 2,400 3 16 292,500 2,334 3 26 295,000 2,411 3 21 353,000 2,477 3 9 355,000 2,936 4 8 388,000 2.640 3 1 392,500 2,670 4 2 394,000 2,679 3 3 395,000 2,510 3 1 404,000 2,800 4 2 565,500 3,262 5 8 431,000 2,854 4 2Explanation / Answer
-790.647
p value of square footage=0.68
Exclude the variable and run regression
So after the excluding square footage variable and conducting the regression analsysis we got
selling price= 166143.8825+68786.33861(Bed rooms)-3109.153925(Age)
R2 =0.81
81% variation in selling price is explained by model.
Good model
s to predict the selling price of a 9-year-old, 3.030 square foot house with 4 bedrooms.
Substitue in the regression equation age=9 yrs
bedrooms=4
selling price= 166143.8825+68786.33861(Bed rooms)--3109.153925(Age)
selling price= 166143.8825+68786.33861(4)--3109.153925(9)
=469271.622265
SUMMARY OUTPUT Regression Statistics Multiple R 0.901181361 R Square 0.812127845 Adjusted R Square 0.760889985 Standard Error 39633.63098 Observations 15 ANOVA df SS MS F Significance F Regression 3 7.47E+10 2.49E+10 15.85015 0.000263 Residual 11 1.73E+10 1.57E+09 Total 14 9.2E+10 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 165639.8305 61637.61 2.687318 0.02113 29976.36 301303.3 square footage -7.121307674 17.05089 -0.41765 0.684239 -44.6501 30.40744 Bedrooms 73689.20512 20068.33 3.671915 0.003677 29519.11 117859.3 Age -3059.716002 1030.933 -2.96791 0.012793 -5328.79-790.647
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