정리를 위한 블로그

> set.seed(2)

> u <- runif(10,0,11)

> v <- runif(10,11,20)

> w <- runif(10,1,30)

> y = 3+0.1*u+2*v -3*w + rnorm(10,0,0.1)

> dfrm = data.frame(y,u,v,w)

> dfrm

             y         u        v         w

1  -25.6647952  2.033705 15.97407 20.195064

2   -6.5562326  7.726114 13.15005 12.238937

3  -36.4858791  6.306590 17.84462 25.269786

4   12.4472764  1.848571 12.62738  5.364542

5    0.1638434 10.382233 14.64754 11.070895

6   -3.9124946 10.378225 18.68194 15.174424

7   26.6127780  1.420749 19.78759  5.328159

8   -3.9238295  9.167937 13.03243 11.354815

9  -53.0331805  5.148204 15.00328 28.916677

10  12.4387413  6.049821 11.67481  4.838788

> m <- lm(y~u+v+w)

> m

Call:

lm(formula = y ~ u + v + w)

Coefficients:

(Intercept)            u            v            w  

     3.0417       0.1232       1.9890      -2.9978  

> summary(m)

Call:

lm(formula = y ~ u + v + w)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.188562 -0.058632 -0.002013  0.080024  0.143757 

Coefficients:

             Estimate Std. Error  t value Pr(>|t|)    

(Intercept)  3.041653   0.264808   11.486 2.62e-05 ***

u            0.123173   0.012841    9.592 7.34e-05 ***

v            1.989017   0.016586  119.923 2.27e-11 ***

w           -2.997816   0.005421 -552.981 2.36e-15 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1303 on 6 degrees of freedom

Multiple R-squared:      1, Adjusted R-squared:      1 

F-statistic: 1.038e+05 on 3 and 6 DF,  p-value: 1.564e-14