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.
Q5. Run a multiple regression model that predicts price based on SqFt and number of Bedrooms. In this model both predictors are statistically insignificant. TRUE or FALSE
| 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 to run the multiple regression model that predicts the price based on Sq Ft and the number of Bedrooms. The excel output is given below:
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.6269 | |||||
| R Square | 0.3930 | |||||
| Adjusted R Square | 0.3832 | |||||
| Standard Error | 42202.0994 | |||||
| Observations | 128 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 2 | 144113707570.10 | 72056853785.05 | 40.458 | 0.0000 | |
| Residual | 125 | 222627149617.40 | 1781017196.94 | |||
| Total | 127 | 366740857187.50 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | -12735.193 | 35655.830 | -0.357 | 0.722 | -83302.504 | 57832.118 |
| Sq Ft | 98.998 | 20.225 | 4.895 | 0.000 | 58.971 | 139.025 |
| Bedrooms | 24972.116 | 5894.269 | 4.237 | 0.000 | 13306.627 | 36637.605 |
Q5. Run a multiple regression model that predicts the price based on SqFt and the number of Bedrooms. In this model, both predictors are statistically insignificant.
Answer: FALSE, because the p-value for both these variables is less than the significance level. Therefore, both the variables are significant
orchestra answered 2 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
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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
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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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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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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