Question
In: Statistics and Probability
The data below id from 128 recent sales in Mid City. For each sale, it shows...
The data below id from 128 recent sales in Mid City. For each sale, it shows the neighborhood (1, 2, or 3) of the house, # of offers made on the house, sq. footage, whether house is primarily made of brick, # of bathrooms, # of bedrooms, & selling price. Â Neghborhoods 1 & 2 are traditional, 3 is newer/more prestigious. Use regression to estimate & interpret the pricing structure of houses in Mid City.
Q4. Based on the regression of SqFt on Price, each 1 SqFt increase raises the Price by ______.
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-20182.3 |
||
|
0.3 |
||
|
7.4 |
||
|
140.5 |
| Home | Nbhd | Offers | Sq Ft | Brick | Bedrooms | Bathrooms | Price |
| 1 | 2 | 2 | 1790 | No | 2 | 2 | 228600 |
| 2 | 2 | 3 | 2030 | No | 4 | 2 | 228400 |
| 3 | 2 | 1 | 1740 | No | 3 | 2 | 229600 |
| 4 | 2 | 3 | 1980 | No | 3 | 2 | 189400 |
| 5 | 2 | 3 | 2130 | No | 3 | 3 | 239600 |
| 6 | 1 | 2 | 1780 | No | 3 | 2 | 229200 |
| 7 | 3 | 3 | 1830 | Yes | 3 | 3 | 303200 |
| 8 | 3 | 2 | 2160 | No | 4 | 2 | 301400 |
| 9 | 2 | 3 | 2110 | No | 4 | 2 | 238400 |
| 10 | 2 | 3 | 1730 | No | 3 | 3 | 208000 |
| 11 | 2 | 3 | 2030 | Yes | 3 | 2 | 265000 |
| 12 | 2 | 2 | 1870 | Yes | 2 | 2 | 246000 |
| 13 | 1 | 4 | 1910 | No | 3 | 2 | 205200 |
| 14 | 1 | 5 | 2150 | Yes | 3 | 3 | 252600 |
| 15 | 3 | 4 | 2590 | No | 4 | 3 | 353600 |
| 16 | 3 | 1 | 1780 | No | 4 | 2 | 291600 |
| 17 | 2 | 4 | 2190 | Yes | 3 | 3 | 294200 |
| 18 | 1 | 4 | 1990 | No | 3 | 3 | 167200 |
| 19 | 2 | 1 | 1700 | Yes | 2 | 2 | 222800 |
| 20 | 3 | 2 | 1920 | Yes | 3 | 3 | 334400 |
| 21 | 2 | 3 | 1790 | No | 3 | 2 | 232400 |
| 22 | 1 | 4 | 2000 | No | 3 | 2 | 227600 |
| 23 | 1 | 3 | 1690 | No | 3 | 2 | 183400 |
| 24 | 1 | 3 | 1820 | Yes | 3 | 2 | 212200 |
| 25 | 2 | 2 | 2210 | Yes | 4 | 3 | 312800 |
| 26 | 1 | 3 | 2290 | No | 4 | 3 | 298600 |
| 27 | 3 | 3 | 2000 | No | 4 | 2 | 274000 |
| 28 | 2 | 2 | 1700 | No | 3 | 2 | 198600 |
| 29 | 1 | 3 | 1600 | No | 2 | 2 | 138200 |
| 30 | 3 | 1 | 2040 | Yes | 4 | 3 | 376000 |
| 31 | 3 | 3 | 2250 | Yes | 4 | 3 | 364000 |
| 32 | 1 | 2 | 1930 | Yes | 2 | 2 | 224600 |
| 33 | 2 | 3 | 2250 | Yes | 3 | 3 | 270000 |
| 34 | 2 | 4 | 2280 | Yes | 5 | 3 | 279200 |
| 35 | 1 | 3 | 2000 | No | 2 | 2 | 235600 |
| 36 | 1 | 3 | 2080 | No | 3 | 3 | 234200 |
| 37 | 1 | 2 | 1880 | No | 2 | 2 | 235000 |
| 38 | 3 | 4 | 2420 | No | 4 | 3 | 294000 |
| 39 | 3 | 1 | 1720 | No | 3 | 2 | 262600 |
| 40 | 1 | 2 | 1740 | No | 3 | 2 | 216400 |
| 41 | 2 | 1 | 1560 | No | 2 | 2 | 213200 |
| 42 | 3 | 2 | 1840 | No | 4 | 3 | 267200 |
| 43 | 2 | 3 | 1990 | No | 2 | 2 | 211200 |
| 44 | 2 | 1 | 1920 | Yes | 3 | 2 | 308000 |
| 45 | 3 | 2 | 1940 | Yes | 3 | 3 | 333000 |
| 46 | 2 | 3 | 1810 | No | 3 | 2 | 206400 |
| 47 | 1 | 2 | 1990 | No | 2 | 3 | 259600 |
| 48 | 1 | 6 | 2050 | No | 3 | 2 | 180600 |
| 49 | 2 | 2 | 1980 | No | 2 | 2 | 231800 |
| 50 | 1 | 3 | 1700 | Yes | 3 | 2 | 215000 |
| 51 | 2 | 3 | 2100 | Yes | 3 | 2 | 302200 |
| 52 | 1 | 3 | 1860 | No | 2 | 2 | 182200 |
| 53 | 1 | 4 | 2150 | No | 2 | 3 | 234800 |
| 54 | 1 | 3 | 2100 | No | 3 | 2 | 261600 |
| 55 | 1 | 3 | 1650 | No | 3 | 2 | 162600 |
| 56 | 2 | 2 | 1720 | Yes | 2 | 2 | 251400 |
| 57 | 2 | 3 | 2190 | Yes | 3 | 2 | 281800 |
| 58 | 3 | 3 | 2240 | No | 4 | 3 | 304600 |
| 59 | 3 | 1 | 1840 | No | 3 | 3 | 276200 |
| 60 | 3 | 1 | 2090 | No | 4 | 2 | 310800 |
| 61 | 3 | 1 | 2200 | No | 3 | 3 | 361800 |
| 62 | 1 | 2 | 1610 | No | 2 | 2 | 201800 |
| 63 | 3 | 2 | 2220 | No | 4 | 3 | 322600 |
| 64 | 2 | 2 | 1910 | No | 2 | 3 | 241000 |
| 65 | 3 | 2 | 1860 | No | 3 | 2 | 260600 |
| 66 | 1 | 1 | 1450 | Yes | 2 | 2 | 222200 |
| 67 | 1 | 4 | 2210 | No | 3 | 3 | 252400 |
| 68 | 2 | 3 | 2040 | No | 4 | 3 | 303800 |
| 69 | 1 | 4 | 2140 | No | 3 | 2 | 187200 |
| 70 | 3 | 3 | 2080 | No | 4 | 3 | 331200 |
| 71 | 3 | 3 | 1950 | Yes | 3 | 3 | 333400 |
| 72 | 3 | 1 | 2160 | No | 4 | 2 | 315200 |
| 73 | 1 | 3 | 1650 | No | 3 | 2 | 214600 |
| 74 | 2 | 2 | 2040 | No | 3 | 3 | 251400 |
| 75 | 3 | 3 | 2140 | No | 3 | 3 | 288400 |
| 76 | 1 | 2 | 1900 | No | 2 | 2 | 213800 |
| 77 | 3 | 2 | 1930 | No | 3 | 2 | 259600 |
| 78 | 3 | 3 | 2280 | Yes | 4 | 3 | 353000 |
| 79 | 1 | 3 | 2130 | No | 3 | 2 | 242600 |
| 80 | 3 | 1 | 1780 | No | 4 | 2 | 287200 |
| 81 | 2 | 4 | 2190 | Yes | 3 | 3 | 286800 |
| 82 | 3 | 2 | 2140 | Yes | 4 | 3 | 368600 |
| 83 | 3 | 1 | 2050 | Yes | 2 | 2 | 329600 |
| 84 | 2 | 2 | 2410 | No | 3 | 3 | 295400 |
| 85 | 1 | 3 | 1520 | No | 2 | 2 | 181000 |
| 86 | 3 | 2 | 2250 | Yes | 4 | 3 | 376600 |
| 87 | 1 | 4 | 1900 | No | 4 | 2 | 205400 |
| 88 | 3 | 1 | 1880 | Yes | 3 | 3 | 345000 |
| 89 | 1 | 2 | 1930 | No | 3 | 3 | 255400 |
| 90 | 1 | 4 | 2010 | No | 2 | 2 | 195600 |
| 91 | 3 | 2 | 1920 | No | 4 | 2 | 286200 |
| 92 | 2 | 2 | 2150 | No | 3 | 2 | 233000 |
| 93 | 3 | 2 | 2110 | No | 3 | 2 | 285200 |
| 94 | 2 | 2 | 2080 | No | 3 | 3 | 314200 |
| 95 | 3 | 3 | 2150 | Yes | 4 | 3 | 321200 |
| 96 | 3 | 1 | 1970 | Yes | 2 | 2 | 305000 |
| 97 | 2 | 3 | 2440 | No | 3 | 3 | 266600 |
| 98 | 2 | 1 | 2000 | Yes | 2 | 2 | 253600 |
| 99 | 3 | 1 | 2060 | No | 3 | 2 | 291000 |
| 100 | 3 | 2 | 2080 | Yes | 3 | 3 | 342000 |
| 101 | 1 | 5 | 2010 | No | 3 | 2 | 206400 |
| 102 | 2 | 5 | 2260 | No | 3 | 3 | 246200 |
| 103 | 2 | 4 | 2410 | No | 3 | 3 | 273600 |
| 104 | 3 | 3 | 2440 | Yes | 4 | 3 | 422400 |
| 105 | 2 | 4 | 1910 | No | 3 | 2 | 164600 |
| 106 | 3 | 4 | 2530 | No | 4 | 3 | 293800 |
| 107 | 1 | 4 | 2130 | No | 3 | 2 | 217000 |
| 108 | 2 | 1 | 1890 | Yes | 3 | 2 | 268000 |
| 109 | 2 | 3 | 1990 | Yes | 3 | 3 | 234000 |
| 110 | 2 | 3 | 2110 | No | 3 | 2 | 217400 |
| 111 | 1 | 1 | 1710 | No | 2 | 2 | 223200 |
| 112 | 1 | 2 | 1740 | No | 2 | 2 | 229800 |
| 113 | 2 | 2 | 1940 | Yes | 2 | 2 | 247200 |
| 114 | 1 | 3 | 2000 | Yes | 3 | 2 | 231400 |
| 115 | 2 | 2 | 2010 | No | 4 | 3 | 249000 |
| 116 | 1 | 3 | 1900 | No | 3 | 3 | 205000 |
| 117 | 3 | 1 | 2290 | Yes | 5 | 4 | 399000 |
| 118 | 1 | 2 | 1920 | No | 3 | 2 | 235600 |
| 119 | 1 | 3 | 1950 | Yes | 3 | 2 | 300400 |
| 120 | 1 | 4 | 1920 | No | 2 | 2 | 219400 |
| 121 | 1 | 3 | 1930 | No | 2 | 3 | 220800 |
| 122 | 2 | 3 | 1930 | No | 3 | 3 | 211200 |
| 123 | 2 | 1 | 2060 | Yes | 2 | 2 | 289600 |
| 124 | 2 | 3 | 1900 | Yes | 3 | 3 | 239400 |
| 125 | 2 | 3 | 2160 | Yes | 4 | 3 | 295800 |
| 126 | 1 | 2 | 2070 | No | 2 | 2 | 227000 |
| 127 | 3 | 1 | 2020 | No | 3 | 3 | 299800 |
| 128 | 1 | 4 | 2250 | No | 3 | 3 | 249200 |
Solutions
Expert Solution
Solution: We can use the excel regression data analysis tool for running the regression of Price on Sq FT to find the coefficient of the slope.
Data> Data Analysis> Regression>
input Y range = Price Data
input x range = Sq Ft data
The excel output is given below:
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.5530 | |||||
| R Square | 0.3058 | |||||
| Adjusted R Square | 0.3003 | |||||
| Standard Error | 44951.0673 | |||||
| Observations | 128 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 1 | 112145452218.5470 | 112145452218.5470 | 55.5011 | 0.0000 | |
| Residual | 126 | 254595404968.9530 | 2020598452.1346 | |||
| Total | 127 | 366740857187.5000 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | -20182.260 | 37932.208 | -0.532 | 0.596 | -95248.983 | 54884.464 |
| Sq Ft | 140.5 | 18.853 | 7.450 | 0.000 | 103.143 | 177.762 |
Q4. Based on the regression of SqFt on Price, each 1 SqFt increase raises the Price by 140.5.
Answer: 140.5
orchestra answered 3 years agoRelated Solutions
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The data below id from 128 recent sales in Mid City. For each
sale, it shows the neighborhood (1, 2, or 3) of the house, # of
offers made on the house, sq. footage, whether house is primarily
made of brick, # of bathrooms, # of bedrooms, & selling price.
Neghborhoods 1 & 2 are traditional, 3 is newer/more
prestigious. Use regression to estimate & interpret the
pricing structure of houses in Mid City.
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The data below id from 128 recent sales in Mid City. For each sale, it shows...
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sale, it shows the neighborhood (1, 2, or 3) of the house, # of
offers made on the house, sq. footage, whether house is primarily
made of brick, # of bathrooms, # of bedrooms, & selling price.
 Neghborhoods 1 & 2 are traditional, 3 is newer/more
prestigious. Use regression to estimate & interpret the
pricing structure of houses in Mid City.
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The data below id from 128 recent sales in Mid City. For each sale, it shows...
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sale, it shows the neighborhood (1, 2, or 3) of the house, # of
offers made on the house, sq. footage, whether house is primarily
made of brick, # of bathrooms, # of bedrooms, & selling price.
 Neghborhoods 1 & 2 are traditional, 3 is newer/more
prestigious. Use regression to estimate & interpret the
pricing structure of houses in Mid City.
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offers made on the house, sq. footage, whether house is primarily
made of brick, # of bathrooms, # of bedrooms, & selling price.
 Neghborhoods 1 & 2 are traditional, 3 is newer/more
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