> a <- c(55, 53, 52.7, 59.5, 57, 55, 56.5, 57, 56, 57, 57, 58.6, 52, 54, 55, 56, 51)
> b1 <- c(15.6, 14.3, 14.8, 17.5, 16.5, 16, 15.3, 15.6, 16.7, 17, 16.5, 16.5, 15, 15, 15.9, 16, 14.2)
> b2 <- c(14.6, 15.4, 14.5, 16.4, 15.8, 15.5, 15.9, 16.2, 15.1, 16, 15.6, 16, 14, 15.6, 15.2, 14.8, 14.3)
> y<-data.frame(a,b1,b2)
> y
a b1 b2
1 55.0 15.6 14.6
2 53.0 14.3 15.4
3 52.7 14.8 14.5
4 59.5 17.5 16.4
5 57.0 16.5 15.8
6 55.0 16.0 15.5
7 56.5 15.3 15.9
8 57.0 15.6 16.2
9 56.0 16.7 15.1
10 57.0 17.0 16.0
11 57.0 16.5 15.6
12 58.6 16.5 16.0
13 52.0 15.0 14.0
14 54.0 15.0 15.6
15 55.0 15.9 15.2
16 56.0 16.0 14.8
17 51.0 14.2 14.3
>
>
> y.lm<-lm(a~.,data=y)
> y.lm
Call:
lm(formula = a ~ ., data = y)
Coefficients:
(Intercept) b1 b2
8.588 1.465 1.545
> summary(y.lm)
Call:
lm(formula = a ~ ., data = y)
Residuals:
Min 1Q Median 3Q Max
-1.2134 -0.3833 -0.1719 0.5284 1.1191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.5882 4.2828 2.005 0.06466 .
b1 1.4649 0.2522 5.809 4.53e-05 ***
b2 1.5451 0.3366 4.590 0.00042 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7656 on 14 degrees of freedom
Multiple R-squared: 0.9041, Adjusted R-squared: 0.8904
F-statistic: 66.03 on 2 and 14 DF, p-value: 7.436e-08
コメント（35）
2019
