> dat3 <- read.csv("Table923dum.csv")
> dat3
Y X1 X2 X3 X4 X5 X6 X7 X8
1 44000 95 59.2 10.0 80 7 8 125 O
2 50000 95 62.5 15.0 200 14 12 200 O
3 110000 90 54.8 15.0 98 0 15 250 N
4 16000 12 54.5 10.0 60 5 6 70 O
5 10000 66 48.0 13.0 42 3 3 40 O
6 160000 95 67.5 5.0 100 18 17 200 N
7 100000 95 70.5 8.0 100 3 4 150 N
8 6500 65 48.3 12.0 50 4 3 50 N
9 1500 66 51.0 8.0 16 2 2 25 N
10 164000 138 75.0 7.0 200 1 26 220 N
11 1300 19 43.0 23.0 18 1 0 20 O
12 13000 62 67.0 22.0 75 0 0 120 O
13 40000 66 60.3 0.5 100 7 6 130 O
14 2200 22 47.5 17.0 34 3 1 30 O
15 50000 95 62.9 5.0 80 2 4 110 N
16 200000 95 68.7 7.0 90 3 18 300 O
> model7=lm(Y~X6+X7+factor(X8),data=dat3)
> summary(model7)
Call:
lm(formula = Y ~ X6 + X7 + factor(X8), data = dat3)
Residuals:
Min 1Q Median 3Q Max
-47251 -9336 -4972 14262 40897
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9060.5 15126.1 -0.599 0.5603
X6 3160.0 1685.4 1.875 0.0853 .
X7 428.9 144.1 2.976 0.0116 *
factor(X8)O -17391.8 13916.2 -1.250 0.2352
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26320 on 12 degrees of freedom
Multiple R-squared: 0.8723, Adjusted R-squared: 0.8403
F-statistic: 27.31 on 3 and 12 DF, p-value: 1.203e-05
>
> step(model7)
Start: AIC=329.1
Y ~ X6 + X7 + factor(X8)
Df Sum of Sq RSS AIC
- factor(X8) 1 1082129865 9.3962e+09 329.06
<none> 8.3141e+09 329.10
- X6 1 2435713383 1.0750e+10 331.21
- X7 1 6134816901 1.4449e+10 335.94
Step: AIC=329.06
Y ~ X6 + X7
Df Sum of Sq RSS AIC
<none> 9.3962e+09 329.06
- X6 1 3564006627 1.2960e+10 332.20
- X7 1 5602366191 1.4999e+10 334.54
Call:
lm(formula = Y ~ X6 + X7, data = dat3)
Coefficients:
(Intercept) X6 X7
-20210.8 3696.5 406.8