1) Using the data set in the sheet labeled \'raw data\' perform regression ananl
ID: 3206519 • Letter: 1
Question
1) Using the data set in the sheet labeled 'raw data' perform regression ananlysis using the variable labeled 'Selling Price' as your dependent variable and the remaining variables as your independednt variables. (You need to run the analysis only once. There is NO step wise regrssion required here)
2.) What are the important variables that predict Selling Price of houses? How did you identify them to be important/significant variables?
3.) Is the model a good model for predicting the Selling Price of houses? If yes, why? If no, why not?
4.)List the significant variables that predict Selling Price of houses in the order of their importance? Explain why you ordered them this way.
1) Using the data set in the sheet labeled 'raw data' perform regression ananlysis using the variable labeled 'Selling Price' as your dependent variable and the remaining variables as your independednt variables. (You need to run the analysis only once. There is NO step wise regrssion required here)
2.) What are the important variables that predict Selling Price of houses? How did you identify them to be important/significant variables?
3.) Is the model a good model for predicting the Selling Price of houses? If yes, why? If no, why not?
4.)List the significant variables that predict Selling Price of houses in the order of their importance? Explain why you ordered them this way.
Selling Price No. of Garages Square Footage Days on Market No. of Rooms No. of fireplaces No. of Baths Heat (Gas=1; electric=0) City/ Suburb (City=1;Suburb=0) $57,900 2 1,340 29 7 1 2 1 0 $67,900 2 1,300 20 7.5 1 1.5 1 0 $70,500 2 1,650 135 8.5 0 1.5 1 0 $72,000 2 2,000 188 10.5 1 2.5 0 0 $85,000 2 2,200 99 10.5 1 2.5 1 0 $90,000 1 2,000 21 8.5 1 1.5 1 0 $92,000 1 2,400 81 9 1 2 1 0 $93,000 2 2,300 175 10.5 2 2.5 1 0 $49,000 1 1,150 51 8 0 1 1 0 $66,900 2 1,500 14 9 1 2 1 0 $67,000 2 2,200 105 10.5 1 2.5 0 0 $73,000 2 1,900 67 10 0 2 1 0 $84,000 2 1,616 125 8.5 1 2.5 0 0 $86,000 2 2,000 34 10 1 2 1 0 $88,000 2 1,350 90 8 1 2 0 0 $105,000 2 4,000 182 11.5 1 2.5 1 0 $106,900 2 2,060 160 9.5 1 2.5 0 0 $210,000 2 3,991 133 13 3 3 0 0 $45,900 0 1,400 34 8 0 1 1 0 $48,500 1 860 25 7 0 1 1 0 $50,500 1 1,000 7 7 1 1 1 0 $57,900 1 1,442 104 7 1 1 1 0 $58,500 1 1,378 2 9.5 1 1.5 1 0 $59,000 0 1,270 46 8 1 1 0 0 $70,000 2 2,000 52 10.5 1 2.5 0 0 $70,000 2 2,000 49 10.5 1 2.5 1 0 $80,000 2 2,200 153 10.5 1 2.5 1 0 $81,000 2 1,800 35 11 1 2 0 0 $81,900 2 1,782 13 10.5 1 2.5 1 0 $82,000 2 2,040 58 10.5 1 2.5 1 0 $82,900 2 2,082 33 11.5 1 2.5 1 0 $84,250 2 2,400 7 13.5 1 2.5 1 0 $84,500 2 2,000 2 11.5 1 2.5 1 0 $86,000 2 2,000 29 11.5 1 2.5 1 0 $87,500 2 1,900 84 9 2 2 1 0 $90,000 2 2,075 118 10 2 2 1 0 $90,000 2 2,000 51 11.5 1 2.5 1 0 $90,000 2 2,200 76 11.5 1 2.5 1 0 $118,000 2 2,460 35 11 1 3 1 0 $145,000 2.5 2,500 93 11.5 3 2.5 0 0 $30,500 0 931 79 9 1 1 1 1 $43,000 2 1,263 28 8 1 1 1 1 $44,000 1 1,200 32 8.5 1 2.5 1 1 $44,900 0 1,500 140 8 0 1 0 1 $48,500 0 1,500 11 9 0 1 1 1 $59,000 1.5 1,600 66 9 1 2 1 1 $59,100 2 2,000 108 9.5 1 1.5 0 1 $59,900 2.5 1,900 178 11 1 2 1 1 $60,500 2 1,800 58 7.5 1 1.5 1 1 $67,500 1.5 1,460 74 8.5 1 1.5 1 1 $67,500 2.5 1,600 21 10 1 2 1 1 $70,000 3 1,800 27 11.5 1 2.5 1 1 $85,000 2 2,800 53 10.5 2 2.5 1 1 $92,000 2 3,029 60 10 2 2 1 1 $40,000 0 1,464 54 7 0 1 1 1 $40,500 0 1,300 133 8 0 1 1 1 $41,000 1 1,011 63 7 1 2 1 1 $42,900 0 1,218 15 8 0 1 1 1 $42,900 1 1,092 47 7 1 1 1 1 $43,500 0 1,500 20 7 0 1 1 1 $44,950 1.5 1,500 88 8 1 1 1 1 $46,000 2 1,125 18 8.5 1 1.5 1 1 $47,000 2 1,118 46 8.5 0 1.5 1 1 $53,000 2 1,323 24 7 0 1 0 1 $56,600 2 1,700 89 9 1 2 1 1 $56,600 2 1,600 30 9 1 2 0 1 $56,900 1.5 1,456 62 8.5 1 1.5 1 1 $56,900 1.5 1,200 16 7 0 1 1 1 $56,900 1 1,420 15 7.5 1 1.5 1 1 $58,000 2 1,850 51 9.5 1 1.5 0 1 $59,500 2 1,800 113 11 2 2 1 1 $59,900 2 1,450 80 8.5 1 1.5 1 1 $66,500 2 1,600 28 8.5 1 1.5 1 1 $67,000 1 2,000 20 8.5 2 2.5 1 1 $68,500 2 2,000 35 10 1 3 1 1 $70,000 1 2,250 55 12 1 3 1 1 $72,000 2 1,800 18 9 1 2 1 1 $72,500 1 2,200 30 11.5 1 2.5 1 1 $75,900 2 1,719 19 9.5 1 2.5 1 1 $76,900 2 1,768 75 8.5 1 2.5 0 1 $89,500 2 1,851 120 10 2 2 1 1 $94,000 2 2,350 57 10.5 1 2.5 1 1 $125,000 2.5 2,350 67 9.5 2 2.5 1 1 $99,500 2 2,200 21 11 2 3 1 1 $75,000 2 1,435 61 9 1 2 0 1 $74,600 1 2,000 98 8.5 1 2.5 0 1 $66,000 3 2,300 30 8.5 2 1.5 1 1 $65,000 2 1,744 23 9 1 2 1 1 $64,700 2.5 1,728 87 10.5 0 2.5 1 1 $60,000 1 2,000 26 10 1 2 1 1Explanation / Answer
1) Using the data set in the sheet labeled 'raw data' perform regression ananlysis using the variable labeled 'Selling Price' as your dependent variable and the remaining variables as your independednt variables. (You need to run the analysis only once. There is NO step wise regrssion required here)
SUMMARY OUTPUT Regression Statistics Multiple R 0.883140563 R Square 0.779937253 Adjusted R Square 0.758202661 Standard Error 12343.47025 Observations 90 ANOVA df SS MS F Significance F Regression 8 43739388115 5.47E+09 35.8846 1.46E-23 Residual 81 12341261885 1.52E+08 Total 89 56080650000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 26525.64491 9999.119809 2.652798 0.009603 6630.533 46420.76 6630.533 46420.76 X Variable 1 2142.460415 2285.95842 0.937226 0.351428 -2405.88 6690.801 -2405.88 6690.801 X Variable 2 24.64448088 4.030271483 6.114844 3.24E-08 16.6255 32.66346 16.6255 32.66346 X Variable 3 -32.7831164 32.06019079 -1.02255 0.309565 -96.5728 31.00661 -96.5728 31.00661 X Variable 4 -995.5856683 1523.584788 -0.65345 0.515317 -4027.04 2035.87 -4027.04 2035.87 X Variable 5 10754.18201 2712.023918 3.965371 0.000157 5358.105 16150.26 5358.105 16150.26 X Variable 6 5288.50854 3649.190346 1.449228 0.151135 -1972.24 12549.25 -1972.24 12549.25 X Variable 7 -7623.362189 3446.562978 -2.21187 0.029792 -14480.9 -765.783 -14480.9 -765.783 X Variable 8 -12117.48781 2754.743278 -4.39877 3.28E-05 -17598.6 -6636.41 -17598.6 -6636.41Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.