Econometrics/Spring 2017/Linear regression model / Powered by R / Apartment example: dummy variables / R_LMM_dummy_variables.sagews
231 viewsФиктивные переменные
'data.frame': 100 obs. of 10 variables:
$ X : int 1 2 3 4 5 6 7 8 9 10 ...
$ Y : num 15.9 27 21.1 24.5 13.5 22.5 15.5 75.9 15.1 26 ...
$ X1: int 1 3 2 4 1 2 3 4 1 2 ...
$ X2: Factor w/ 4 levels "К","М","П","С": 4 1 4 4 1 1 4 3 1 1 ...
$ X3: num 39 68.4 54.7 90 34.8 48 68.1 132 39 55.5 ...
$ X4: num 20 40.5 28 64 16 29 44.4 89.6 20 35 ...
$ X5: num 8.2 10.7 10.7 15 10.7 8 7.2 11 8.5 8 ...
$ X6: int 0 0 0 0 0 1 0 1 0 0 ...
$ X7: int 1 1 1 0 0 1 0 1 1 1 ...
$ X8: Factor w/ 2 levels "В","Н": 2 2 2 1 2 1 1 2 2 1 ...
| (Intercept) | X8.fН | |
|---|---|---|
| 1 | 1 | 1 |
| 2 | 1 | 1 |
| 3 | 1 | 1 |
| 4 | 1 | 0 |
| 5 | 1 | 1 |
| 6 | 1 | 0 |
| 7 | 1 | 0 |
| 8 | 1 | 1 |
| 9 | 1 | 1 |
| 10 | 1 | 0 |
| 11 | 1 | 0 |
| 12 | 1 | 0 |
| 13 | 1 | 1 |
| 14 | 1 | 0 |
| 15 | 1 | 1 |
| 16 | 1 | 1 |
| 17 | 1 | 0 |
| 18 | 1 | 1 |
| 19 | 1 | 1 |
| 20 | 1 | 1 |
| 21 | 1 | 0 |
| 22 | 1 | 0 |
| 23 | 1 | 1 |
| 24 | 1 | 1 |
| 25 | 1 | 1 |
| 26 | 1 | 0 |
| 27 | 1 | 0 |
| 28 | 1 | 0 |
| 29 | 1 | 1 |
| 30 | 1 | 1 |
| ⋮ | ⋮ | ⋮ |
| 71 | 1 | 1 |
| 72 | 1 | 0 |
| 73 | 1 | 0 |
| 74 | 1 | 0 |
| 75 | 1 | 1 |
| 76 | 1 | 1 |
| 77 | 1 | 1 |
| 78 | 1 | 0 |
| 79 | 1 | 0 |
| 80 | 1 | 1 |
| 81 | 1 | 0 |
| 82 | 1 | 0 |
| 83 | 1 | 0 |
| 84 | 1 | 1 |
| 85 | 1 | 1 |
| 86 | 1 | 1 |
| 87 | 1 | 0 |
| 88 | 1 | 0 |
| 89 | 1 | 0 |
| 90 | 1 | 1 |
| 91 | 1 | 1 |
| 92 | 1 | 1 |
| 93 | 1 | 0 |
| 94 | 1 | 0 |
| 95 | 1 | 0 |
| 96 | 1 | 1 |
| 97 | 1 | 1 |
| 98 | 1 | 1 |
| 99 | 1 | 1 |
| 100 | 1 | 0 |
| (Intercept) | X2.fМ | X2.fП | X2.fС | |
|---|---|---|---|---|
| 1 | 1 | 0 | 0 | 1 |
| 2 | 1 | 0 | 0 | 0 |
| 3 | 1 | 0 | 0 | 1 |
| 4 | 1 | 0 | 0 | 1 |
| 5 | 1 | 0 | 0 | 0 |
| 6 | 1 | 0 | 0 | 0 |
| 7 | 1 | 0 | 0 | 1 |
| 8 | 1 | 0 | 1 | 0 |
| 9 | 1 | 0 | 0 | 0 |
| 10 | 1 | 0 | 0 | 0 |
| 11 | 1 | 0 | 1 | 0 |
| 12 | 1 | 0 | 0 | 0 |
| 13 | 1 | 0 | 0 | 0 |
| 14 | 1 | 1 | 0 | 0 |
| 15 | 1 | 1 | 0 | 0 |
| 16 | 1 | 0 | 0 | 1 |
| 17 | 1 | 1 | 0 | 0 |
| 18 | 1 | 0 | 0 | 0 |
| 19 | 1 | 0 | 1 | 0 |
| 20 | 1 | 1 | 0 | 0 |
| 21 | 1 | 0 | 0 | 1 |
| 22 | 1 | 0 | 0 | 0 |
| 23 | 1 | 0 | 1 | 0 |
| 24 | 1 | 0 | 1 | 0 |
| 25 | 1 | 0 | 1 | 0 |
| 26 | 1 | 1 | 0 | 0 |
| 27 | 1 | 0 | 1 | 0 |
| 28 | 1 | 0 | 0 | 0 |
| 29 | 1 | 0 | 0 | 1 |
| 30 | 1 | 0 | 1 | 0 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| 71 | 1 | 0 | 0 | 1 |
| 72 | 1 | 1 | 0 | 0 |
| 73 | 1 | 1 | 0 | 0 |
| 74 | 1 | 0 | 1 | 0 |
| 75 | 1 | 1 | 0 | 0 |
| 76 | 1 | 0 | 1 | 0 |
| 77 | 1 | 0 | 1 | 0 |
| 78 | 1 | 0 | 0 | 1 |
| 79 | 1 | 0 | 0 | 1 |
| 80 | 1 | 0 | 0 | 0 |
| 81 | 1 | 0 | 1 | 0 |
| 82 | 1 | 0 | 0 | 1 |
| 83 | 1 | 0 | 0 | 1 |
| 84 | 1 | 1 | 0 | 0 |
| 85 | 1 | 1 | 0 | 0 |
| 86 | 1 | 0 | 1 | 0 |
| 87 | 1 | 1 | 0 | 0 |
| 88 | 1 | 0 | 0 | 1 |
| 89 | 1 | 0 | 0 | 1 |
| 90 | 1 | 0 | 1 | 0 |
| 91 | 1 | 1 | 0 | 0 |
| 92 | 1 | 0 | 1 | 0 |
| 93 | 1 | 0 | 0 | 0 |
| 94 | 1 | 0 | 0 | 1 |
| 95 | 1 | 0 | 0 | 1 |
| 96 | 1 | 1 | 0 | 0 |
| 97 | 1 | 0 | 1 | 0 |
| 98 | 1 | 1 | 0 | 0 |
| 99 | 1 | 1 | 0 | 0 |
| 100 | 1 | 1 | 0 | 0 |
Call:
lm(formula = ap$Y ~ ap$X1 + ap$X3 + ap$X4 + ap$X5 + dummy8[,
2])
Residuals:
Min 1Q Median 3Q Max
-12.1940 -4.0914 0.2285 3.0462 15.8874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.8049 1.8582 -1.509 0.1345
ap$X1 -1.9545 1.0562 -1.850 0.0674 .
ap$X3 0.6377 0.1201 5.308 7.38e-07 ***
ap$X4 -0.1911 0.1493 -1.280 0.2038
ap$X5 -0.3848 0.1973 -1.951 0.0541 .
dummy8[, 2] 8.2635 1.2797 6.457 4.65e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.633 on 94 degrees of freedom
Multiple R-squared: 0.8406, Adjusted R-squared: 0.8321
F-statistic: 99.13 on 5 and 94 DF, p-value: < 2.2e-16