Hello, I am working on a multiple regression assignment on the staistical softwa

Question

Hello, I am working on a multiple regression assignment on the staistical software program called Spss. The goal of the assignment is to Model the impact on the price of a condo given variables such as Sq footage of condo, bedrooms, bathrooms etc. The main variable of importance is wether the condo is within .5km of a starbucks. I have collected my data but the spss results I have so far are confusing/concerning. Could someone take a look at my data and results and tell me if there is something I have done wrong?

Here's the data I have collected from "Myrealtor.ca"

Here's the coefficients output, is there multi collinearity? why are all my values negative as most of this coefficients go against common sense (i.e: why would the price of a condo go down with a 1 unit increase in bedrooms?)

Price($) size #ofBedrooms #ofBathrooms Maintenance Cost Within .5 of starbucks? Close o subway? 249,700 525 1 1 553.95 no no 310,000 524 1 1 444.14 no no 359,000 600 1 1 367 yes no 498,888 677 2 1 413.28 no yes 408,888 967 2 1 402.25 no yes 498,000 691 2 1 405 yes yes 449,000 690 1 1 397.03 yes yes 315,000 620 0 1 342.09 no yes 415,000 760 2 2 567.89 yes no 435,000 800 2 1 531.63 no no 189,000 600 1 1 408 no no 135,000 800 2 1 517 no no 239,000 620 1 1 300.49 yes no 398,000 536 1 1 389 yes yes 449,000 850 1 2 451.78 no yes 149,998 600 2 1 728 no no 159,900 570 2 1 583.77 no no 695,000 1015 3 2 882.43 no no 589,000 658 2 1 514.66 yes yes 325,000 511.51 1 1 451.39 yes yes 3,000,000 1976 3 3 1655.55 yes no 349,900 822 3 2 551.17 no no 549,900 923 2 2 652.6 no yes 479,800 740 2 1 845.19 no yes 727,000 800 2 2 578.64 no yes 295,000 682 1 1 729.02 no yes 325,000 655 1 1 321.06 Y Y 469,000 825 3 2 603.02 Y Y 339,000 550 1 1 372.53 Y N 389,900 625 1 1 434 N Y 399,000 900 2 2 587.27 Y Y 349,000 611 1 1 387.83 N N 498,000 810 3 2 847.87 Y Y 699,000 870 3 2 964.28 Y Y 759,000 1600 2 2 1185.86 N Y 419,900 1300 2 2 535.18 N Y 529,000 1350 3 2 964.88 N N 1,795,000 2400 2 3 1794.85 N N 1,350,000 1800 2 3 964.6 Y N 398,000 811 2 2 449.37 N Y 399,000 655 2 1 427.41 N N 229,000 560 2 1 570 N N 838,000 1550 2 2 828.68 Y N 530,000 1000 3 2 569.5 Y Y 470,000 930 3 2 699.99 N N 338,888 650 1 1 483.17 N Y 618,000 1200 3 2 770.84 N Y 799,900 1500 2 2 918.97 Y Y 388,000 630 1 1 316 Y Y 329,000 780 3 2 610.96 Y Y 338,000 500 1 1 565.41 N N 319,900 656 1 1 444.47 N N 450,000 764 2 2 450 N N 429,000 990 1 1 397.39 N N 349,900 1100 2 2 718.64 N N 1,388,000 2900 3 3 3,133.29 N N 699,000 974 3 2 964.28 yes no 318,888 569 1 1 402.12 yes yes 1,599,900 3000 2 3 2951 no yes 449,880 667 2 2 543.45 yes yes Std. Error Mode (Constant I-262246.532 88153 443 -2.975 .004 439059.947 85433.1 18 sa Feetofcondo 887.297 91.643 799 9.682 000 703 484 107 1.110 1.000 2 (Constant 269698.702 106569.174 -2531 014 483545.394 55852.010 SqFeetofcondo 881.704 102.444 .794 8.607 000 676.135 1087.273 815 Numot Bedrooms 6678.227 52552.453 012 127 899 98775.984 112132.439 815 3 (Constant 275443.1 46 107129.704 -2571 013 490515.001 60371.291 SqFeetofcondo 782.660 158.286 .705 4945 000 464.888 1100.433 .344 NumofBedrooms 22467.681 63515.115 -039 -354 725 -149979.596 10504, 4.234 562 NumotBathrooms 98693.800 1 19965.916 .139 823 415 -1 421 47.825 339535.426 246 (Constant 261 200.770 98632.595 -2.648 011 459310.168 63091.372 NumofBedrooms 50965.188 59089.825 -089 -863 .393 169650.595 67720.218 549 NumofBathrooms 57109.302 11 1097.784 080 514 609 166037.1 64 280255.768 243 MaintenanceCost 692.445 215.885 476 3207 002 258828 1126.063 268 5 (Constant 2637 78.505 104918.955 -2514 015 474621.038 52935.971 NumofBedrooms -51798.351 60625.774 -091 -854 .397 17 3630.405 70033.703 532 NumofBathrooms 57362.576 11 2265.224 081 511 61 168242.838 282967.989 242 Maintenance cost 694.860 220.229 478 3155 003 252.293 1137.427 263 5336.338 68105.096 131 525.977 6 (Constant 278788.087 101577636 -2745 008 483023.612 74552.562 SqFeetofcondo 458.547 182 411 413 2.514 015 91.786 825.309 208 NumofBedrooms .37922.091 58915.274 -067 -644 .523 156379.1 89 80535.008 526 NumofBathrooms 5521.343 112381.156 -008 -049 .961 231 478.801 220436.115 .226 Maintenance cost 702.344 212730 483 3302 002 274.622 1130.067 263 Near subway 27234.550 67533.822 -032 -403 689 163020.399 1 08551.299 897 1 50861.603 70882.103 8343.583 293379.62 Dependent Variable: Price 1.000 226 226 2.910 1.780 4.067 4.728 1.821 4.123 3.725 4.73 1.879 4.126 3.799 1.057 4.800 1.903 4.433 3.800 1.114 1.1 37

Explanation / Answer

The main problem is that the price shows a skewed distribution. If the Price is transformed to the log scale, the histogram appears near symmetric and the signs are not negative.

In the SPSS output, the multicollinearity problem is not indicated.

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