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Lydia Bennet, a realtor in Brownsburg, Indiana, would like to use estimates from a mulitple regression model to help prospective sellers determine a reasonable asking
Lydia Bennet, a realtor in Brownsburg, Indiana, would like to use estimates from a mulitple regression model to help prospective sellers determine a reasonable asking price for their homes. She believes that the following four factors influence the asking price (Price) of a house:
square footage (SQFT)
NUmber of bedrooms (Bed)
Numbers of bathrooms (bath)
Lot Size (LTSZ)
She randomly collects online listings for 50 single-family homes.
Price SQFT Bed Bath LTSZ 399900 5.026 4 4.5 0.3 375000 3.2 4 3 5 372000 3.22 5 3 5 370000 4.927 4 4 0.3 325000 3.904 3 3 1 325000 2.644 3 2.5 5 319500 5.318 3 2.5 2.5 312900 3.144 4 2.5 0.3 299900 2.8 4 3 5 294900 3.804 4 3.5 0.2 269000 3.312 5 3 1 250000 3.373 5 3.5 0.2 249900 3.46 2 2.5 0.6 244994 3.195 4 2.5 0.2 244900 2.914 3 3 0.3 239900 2.881 4 5 0.3 234900 1.772 3 2 3.6 234000 2.248 3 2.5 0.3 229900 3.12 5 2.5 0.2 219900 2.942 4 2.5 0.2 209900 3.332 4 2.5 0.2 209850 3.407 3 2.5 0.3 206900 2.092 3 2 0.3 200000 3.859 4 2 0.2 194900 3.326 4 2.5 0.1 184900 1.874 3 2 0.5 179900 1.892 3 1.5 0.7 179500 2.5 4 2.5 0.5 165000 2.435 4 2.5 0.4 159900 2.714 3 2.5 0.2 159900 1.85 3 2.5 0.5 155000 3.068 4 3.5 0.2 154900 2.484 4 2.5 0.3 152000 1.529 4 2 0.4 149900 2.876 4 2.5 0.2 148500 2.211 4 2.5 0.1 146900 1.571 3 2 0.2 145500 1.503 4 2 0.5 144900 1.656 3 2 0.5 144900 1.521 3 2 0.6 139900 1.315 3 2 0.2 137900 1.706 3 2 0.3 132900 2.121 4 2.5 0.1 129900 1.306 3 2 0.5 129736 1.402 3 2 0.5 125000 1.325 3 2 0.3 119500 1.234 3 2 0.2 110387 1.292 3 1 0.2 106699 1.36 3 1.5 0.1 102900 1.938 3 1 0.1
Provide summary statistics. (10 points) Price SQFI' Bed Bath LTSZ Average Standard Dev. Please round ALL numerical answers to 4 decimal places. Estimate and interpret a multiple regression model where the asking price is the (5 points) response variable and the other four factors are the explanatory variables. Price = [5'0 + SQFT +32 Bed + [1'3Bath + 1841,7152 + s Discuss the coefcients of the four explanatory variables and their meaning within the context of home prices. [12 points] SQFI': Identify and interpret the resulting adjusted R2. 21
Lydia Bennet, a realtor in Brownsburg, Indiana, would like to use estimates from a mulitple regression model to help prospective sellers determine a reasonable asking price for their homes. She believes that the following four factors influence the asking price (Price) of a house: square footage (SQFT) NUmber of bedrooms (Bed) Numbers of bathrooms (bath) Lot Size (LTSZ) She randomly collects online listings for 50 single-family homes. |
Price | SQFT | Bed | Bath | LTSZ |
399900 | 5.026 | 4 | 4.5 | 0.3 |
375000 | 3.2 | 4 | 3 | 5 |
372000 | 3.22 | 5 | 3 | 5 |
370000 | 4.927 | 4 | 4 | 0.3 |
325000 | 3.904 | 3 | 3 | 1 |
325000 | 2.644 | 3 | 2.5 | 5 |
319500 | 5.318 | 3 | 2.5 | 2.5 |
312900 | 3.144 | 4 | 2.5 | 0.3 |
299900 | 2.8 | 4 | 3 | 5 |
294900 | 3.804 | 4 | 3.5 | 0.2 |
269000 | 3.312 | 5 | 3 | 1 |
250000 | 3.373 | 5 | 3.5 | 0.2 |
249900 | 3.46 | 2 | 2.5 | 0.6 |
244994 | 3.195 | 4 | 2.5 | 0.2 |
244900 | 2.914 | 3 | 3 | 0.3 |
239900 | 2.881 | 4 | 5 | 0.3 |
234900 | 1.772 | 3 | 2 | 3.6 |
234000 | 2.248 | 3 | 2.5 | 0.3 |
229900 | 3.12 | 5 | 2.5 | 0.2 |
219900 | 2.942 | 4 | 2.5 | 0.2 |
209900 | 3.332 | 4 | 2.5 | 0.2 |
209850 | 3.407 | 3 | 2.5 | 0.3 |
206900 | 2.092 | 3 | 2 | 0.3 |
200000 | 3.859 | 4 | 2 | 0.2 |
194900 | 3.326 | 4 | 2.5 | 0.1 |
184900 | 1.874 | 3 | 2 | 0.5 |
179900 | 1.892 | 3 | 1.5 | 0.7 |
179500 | 2.5 | 4 | 2.5 | 0.5 |
165000 | 2.435 | 4 | 2.5 | 0.4 |
159900 | 2.714 | 3 | 2.5 | 0.2 |
159900 | 1.85 | 3 | 2.5 | 0.5 |
155000 | 3.068 | 4 | 3.5 | 0.2 |
154900 | 2.484 | 4 | 2.5 | 0.3 |
152000 | 1.529 | 4 | 2 | 0.4 |
149900 | 2.876 | 4 | 2.5 | 0.2 |
148500 | 2.211 | 4 | 2.5 | 0.1 |
146900 | 1.571 | 3 | 2 | 0.2 |
145500 | 1.503 | 4 | 2 | 0.5 |
144900 | 1.656 | 3 | 2 | 0.5 |
144900 | 1.521 | 3 | 2 | 0.6 |
139900 | 1.315 | 3 | 2 | 0.2 |
137900 | 1.706 | 3 | 2 | 0.3 |
132900 | 2.121 | 4 | 2.5 | 0.1 |
129900 | 1.306 | 3 | 2 | 0.5 |
129736 | 1.402 | 3 | 2 | 0.5 |
125000 | 1.325 | 3 | 2 | 0.3 |
119500 | 1.234 | 3 | 2 | 0.2 |
110387 | 1.292 | 3 | 1 | 0.2 |
106699 | 1.36 | 3 | 1.5 | 0.1 |
102900 | 1.938 | 3 | 1 | 0.1 |
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