price assess bdrms lotsize sqrft colonial 300000 349100 4 6126 2438 1 370000 351
ID: 1141045 • Letter: P
Question
price assess bdrms lotsize sqrft colonial 300000 349100 4 6126 2438 1 370000 351500 3 9903 2076 1 191000 217700 3 5200 1374 0 195000 231800 3 4600 1448 1 373000 319100 4 6095 2514 1 466275 414500 5 8566 2754 1 332500 367800 3 9000 2067 1 315000 300200 3 6210 1731 1 206000 236100 3 6000 1767 0 240000 256300 3 2892 1890 0 285000 314000 4 6000 2336 1 300000 416500 5 7047 2634 1 405000 434000 3 12237 3375 1 212000 279300 3 6460 1899 0 265000 287500 3 6519 2312 1 227400 232900 4 3597 1760 1 240000 303800 4 5922 2000 0 285000 305600 3 7123 1774 1 268000 266700 3 5642 1376 1 310000 326000 4 8602 1835 1 266000 294300 3 5494 2048 1 270000 318800 3 7800 2124 1 225000 294200 3 6003 1768 0 150000 208000 4 5218 1732 0 247000 239700 3 9425 1440 1 275000 294100 3 6114 1932 0 230000 267400 3 6710 1932 0 343000 359900 3 8577 2106 1 477500 478100 7 8400 3529 1 350000 355300 4 9773 2051 1 230000 217800 4 4806 1573 1 335000 385000 4 15086 2829 0 251000 224300 3 5763 1630 1 235000 251900 4 6383 1840 1 361000 354900 4 9000 2066 1 190000 212500 4 3500 1702 0 360000 452400 4 10892 2750 1 575000 518100 5 15634 3880 1 209001 289400 4 6400 1854 1 225000 268100 2 8880 1421 0 246000 278500 3 6314 1662 1 713500 655400 5 28231 3331 1 248000 273300 4 7050 1656 1 230000 212100 3 5305 1171 0 375000 354000 5 6637 2293 1 265000 252100 3 7834 1764 1 313000 324000 3 1000 2768 0 417500 475500 4 8112 3733 0 253000 256800 3 5850 1536 1 315000 279200 4 6660 1638 1 264000 313900 3 6637 1972 1 255000 279800 2 15267 1478 0 210000 198700 3 5146 1408 1 180000 221500 3 6017 1812 1 250000 268400 3 8410 1722 1 250000 282300 4 5625 1780 1 209000 230700 4 5600 1674 1 258000 287000 4 6525 1850 1 289000 298700 3 6060 1925 1 316000 314600 4 5539 2343 0 225000 291000 3 7566 1567 0 266000 286400 4 5484 1664 1 310000 253600 6 5348 1386 1 471250 482000 5 15834 2617 1 335000 384300 4 8022 2321 1 495000 543600 4 11966 2638 1 279500 336500 4 8460 1915 1 380000 515100 4 15105 2589 1 325000 437000 4 10859 2709 0 220000 263400 3 6300 1587 1 215000 300400 3 11554 1694 0 240000 250700 3 6000 1536 1 725000 708600 5 31000 3662 0 230000 276300 3 4054 1736 1 306000 388600 2 20700 2205 0 425000 252500 3 5525 1502 0 318000 295200 4 92681 1696 1 330000 359500 3 8178 2186 1 246000 276200 4 5944 1928 1 225000 249800 3 18838 1294 0 111000 202400 4 4315 1535 1 268125 254000 3 5167 1980 1 244000 306800 4 7893 2090 1 295000 318300 3 6056 1837 1 236000 259400 3 5828 1715 0 202500 258100 3 6341 1574 0 219000 232000 2 6362 1185 0 242000 252000 4 4950 1774 1 1. Use the data in HPRICE1 to estimate the following model where price is the house price measured in dollars. (i) Report your estimation output table. Are all of the variables included in the model individually significant at significance level 5%? (i) What is the estimated increase in price for a house with one more bedroom, holding square footage and lot size constant? (iii) What is the estimated increase in price for a house with an additional bedroom that is 300 square feet in size (holding lot size constant)? Compare this to your answer in part (ii) (iv) What percentage of the variation in price is explained by square footage, number of bedrooms and lot size? (v) The second house in the sample has sqrft- 2,076, bdrms 3 and lotsize-9,903. Find the predicted selling price for this house from the estimated model. (vi) The actual selling price of the second house in the sample was S370,000. Find the residual for this house. Does it suggest that the buyer underpaid or overpaid for the house? The Data File: No. of Observations: 88 Variable names (the variable names are listed in the same order as they appear in the data file): 1. price 2. assess 3. bdrms 4. lotsize 5. sqrft 6. colonial price, in dollars assessed value, in dollars number of bedrooms size of lot, square feet size of house, square feet -1 if home is colonial styleExplanation / Answer
i.
Below are regression as estimated in excel:
The estimated regression equation is -
Price = -21770.3086 + 13852.5219*bdrms + 2.0677*lotsize + 122.78*sqrft
Only lotsize and sqrft are significant at 5% level of significance.
ii. Holding sqrft and lotsize constant, a unit increase in bdrms, the increase in the price is expected to be $13852.52.
iii.
The estimated regression equation is -
Price = -21770.3086 + 13852.5219*bdrms + 2.0677*lotsize + 122.78*sqrft
If sqrft = 300 then predicted Price = -21770.3086 + 13852.5219*bdrms + 2.0677*lotsize + 122.78*300
Predicted price = 36834 -21770.3086 + 13852.5219*bdrms + 2.0677*lotsize
To find marginal effect differentiate with respect to bdrms-
DPredicted price/dbdrms = 15063.6914 + 13852.5219*1
= 15063.6914 + 13852.5219
=28916.2133
The expected increase in price is greater in (iii) than in (ii).
(iv) With R-square = 0.67 = 67%, the explanatory variables explain 67% variation in price.
PS: According to Chegg rules, in the event of multiple sub-parts, first 4 subparts are answered.
Regression Statistics Multiple R 0.819976968 R Square 0.672362228 Adjusted R Square 0.660660879 Standard Error 59833.47988 Observations 88