Please help with these questions. Please use Excel to work answers. Thank you.
Using the sample data given in the file P10_10.xlsx, use multiple regression to predict the selling price of houses in a given community. Proceed as follows.
a. Add one explanatory variable at a time and estimate each regression equation along the way. Report and explain changes in the standard error of estimate se, R2, and adjusted R2 as each explanatory variable is added to the model. Does it matter in which order you add the variable? Try at least two different orderings to answer this question.
b. Interpret each of the estimated regression coefficients in the full equation, that is, the equation with all explanatory variables included.
c. What proportion of the total variation in the selling price is explained by the multiple regression equation that includes all four explanatory variables?
Home 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 Price $102,000 $146,300 $182,000 $110,500 $171,900 $154,000 $147,000 $195,900 $183,500 $156,500 $152,000 $170,000 $253,000 $129,500 $241,900 $151,900 $199,000 $186,000 $153,500 $166,000 $224,900 $158,500 $332,000 $172,000 $176,000 $210,000 $156,500 $169,500 $154,900 $163,000 $140,000 $148,500 $224,500 $299,900 $199,900 $220,000 $233,000 $174,900 $124,000 $169,900 $213,000 $165,000 Home Size 600 1050 1800 922 1950 1783 1008 1840 3700 1092 1950 1403 1680 1000 2310 1300 1930 3000 1362 1750 2080 1344 2130 1500 2400 2272 1050 1610 1248 2000 1450 1248 2544 2500 2858 1745 2653 1450 850 1839 2016 1625 Lot Size 0.50 0.43 0.68 0.30 0.75 0.22 0.50 1.16 1.10 0.26 0.50 0.50 14.37 0.49 0.46 0.78 3.00 0.50 0.40 0.50 1.00 0.94 11.91 0.41 0.40 0.41 1.00 0.45 0.22 0.50 0.30 0.25 0.28 0.92 0.79 0.58 1.80 0.30 0.11 2.60 0.78 0.36 Rooms Bathrooms 3 1.0 5 1.5 7 1.5 5 1.0 8 2.5 8 1.5 6 1.0 8 2.0 10 3.0 6 1.0 7 1.5 6 2.0 8 2.0 4 1.0 8 2.5 6 1.0 9 3.0 11 2.5 7 2.0 7 2.0 8 2.5 6 2.0 8 1.5 7 1.0 7 2.5 9 2.5 5 1.0 8 1.5 7 1.0 8 2.0 6 2.0 7 1.0 9 2.5 8 3.0 9 3.0 7 2.5 9 3.0 7 1.0 4 1.0 7 1.5 8 2.5 7 1.5 This isis fictitious fictitious data. data. This 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 $162,000 $211,500 $166,000 $194,000 $192,000 $171,000 $226,800 $155,000 $157,500 $297,000 $315,000 $161,000 $193,500 $163,000 $180,000 $171,000 $163,000 $220,000 $155,900 $219,900 $185,000 $172,500 $167,900 $160,000 $147,000 $210,500 $192,500 $138,000 $200,000 $186,000 $217,000 $180,000 $195,000 $149,000 $165,500 $175,900 $156,000 $235,406 $215,500 $225,000 $155,000 $190,000 $126,000 2000 2250 1300 1956 2496 1575 1960 1200 1296 1950 2516 1066 2276 1908 1122 3500 1100 2300 1118 2464 2100 1552 1856 1800 1248 2000 1848 1036 2277 2300 2080 1600 2680 1200 1526 1680 1232 2465 2800 2265 1300 1900 864 0.11 0.33 0.30 0.50 0.75 0.25 1.33 0.33 0.50 18.70 8.10 0.33 1.00 0.46 3.09 1.00 0.33 5.63 0.56 0.43 0.58 0.46 0.33 0.30 0.30 0.60 0.50 0.95 0.80 0.65 1.23 1.84 0.50 0.25 0.30 0.50 0.31 1.55 1.68 0.85 0.65 1.00 0.32 8 9 7 8 9 7 8 5 9 7 7 5 8 7 5 10 6 7 7 8 8 6 7 7 6 9 7 6 8 7 8 7 9 7 7 6 6 8 9 8 5 8 4 2.0 2.5 1.0 2.5 2.5 1.5 2.5 1.0 1.0 2.5 2.5 1.0 2.5 2.0 2.0 2.5 1.0 2.5 1.5 2.5 1.5 1.5 1.5 1.5 1.0 2.5 2.5 1.0 3.0 3.0 2.5 2.0 3.0 1.0 1.5 1.5 2.0 2.5 1.5 2.5 1.0 2.5 1.0 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 $172,000 $175,000 $181,500 $180,000 $295,000 $146,000 $165,000 $159,000 $138,500 $194,900 $140,000 $184,000 $164,000 $190,000 $250,000 $156,500 $156,500 $188,000 $202,000 $245,000 $171,900 $119,900 $159,900 $165,000 $165,000 $152,500 $265,000 $164,500 $156,500 $210,000 $157,500 $195,000 $127,000 $130,000 $238,000 $212,000 $205,000 $174,900 $207,000 $261,750 $195,000 $108,000 $209,000 2000 1800 1900 1564 2400 1100 1800 1200 1540 1980 1289 1800 1502 2025 3000 1500 1600 1500 2100 2100 1632 1660 1070 1400 1800 1100 3150 2000 1700 1800 1850 2320 1300 1338 2288 2400 2400 1900 2010 2981 1725 821 3060 0.75 0.66 0.75 0.33 2.00 1.10 1.00 0.33 0.18 0.70 0.25 0.68 0.35 1.10 1.15 0.50 0.26 0.54 1.00 0.50 3.00 0.21 1.69 0.35 0.50 0.37 0.30 0.70 0.30 1.52 0.26 0.40 0.37 0.12 1.20 0.50 0.70 0.44 0.68 1.30 1.53 2.30 0.75 9 8 7 6 7 6 8 6 7 8 6 7 7 7 10 7 8 5 8 8 6 7 5 6 7 7 11 8 8 8 9 8 5 6 8 8 8 6 8 10 8 4 8 1.5 2.5 2.0 2.0 2.0 1.0 2.5 1.0 2.0 2.5 1.0 2.0 1.5 2.0 3.5 1.5 1.5 2.5 2.5 2.5 3.0 1.0 1.0 2.0 2.0 1.0 4.0 1.0 2.0 2.5 2.0 2.5 1.0 1.0 2.5 2.5 3.0 2.0 1.5 3.5 2.5 1.0 2.0 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 $115,000 $190,000 $171,000 $215,000 $143,500 $220,000 $137,000 $247,000 $224,500 $182,000 $240,000 $170,000 $150,500 $209,900 $182,500 $189,000 $198,500 $128,000 $147,500 $145,000 $305,000 $220,000 875 1760 2000 2600 1624 2473 1100 3100 2300 1450 2100 1650 1600 2790 1786 1728 1900 1165 1300 1080 2820 2100 0.26 0.05 0.65 0.75 1.80 1.25 0.17 0.54 0.91 0.30 0.50 0.50 0.40 0.75 0.30 0.50 1.06 0.12 0.29 0.31 1.00 1.30 5 7 7 8 7 9 5 10 8 6 8 8 6 13 8 8 7 6 6 5 9 8 1.0 2.0 1.0 2.0 1.5 2.5 1.0 3.5 2.5 1.5 2.5 2.5 2.0 2.5 2.0 1.5 2.5 1.0 1.0 1.0 2.5 1.5 ctitious data. data. ctitious #25, Pg. 449, P10_10.xlsx Using the sample data given in the file P10_1 regression to predict the selling price of hou Proceed as follows. a. Add one explanatory variable at a time an equation along the way. Report and explain error of estimate se, R2, and adjusted R2 as ea added to the model. Does it matter in which variable? Try at least two different orderings b. Interpret each of the estimated regression equation, that is, the equation with all expla c. What proportion of the total variation in t by the multiple regression equation that incl variables? 449, P10_10.xlsx e sample data given in the file P10_10.xlsx, use multiple on to predict the selling price of houses in a given community. as follows. ne explanatory variable at a time and estimate each regression along the way. Report and explain changes in the standard estimate se, R2, and adjusted R2 as each explanatory variable is the model. Does it matter in which order you add the ? Try at least two different orderings to answer this question. ret each of the estimated regression coefficients in the full , that is, the equation with all explanatory variables included. proportion of the total variation in the selling price is explained ultiple regression equation that includes all four explanatory
