\begin{Schunk}
\begin{Sinput}
 dat <- data.frame(x1 = rnorm(1000), x2 = rnorm(1000))
 b0 <- 0.5; b1 <- 0.7; b2 <- 0.15; se <- 10
 dat$y <- b0 + b1*dat$x1 + b2*dat$x2 + se*rnorm(1000)
 m0 <- lm(y ~ x1, data = dat)
 m1 <- lm(y ~ x1 + x2, data = dat)
 summary(m1)
\end{Sinput}
\begin{Soutput}
Call:
lm(formula = y ~ x1 + x2, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
-31.752  -6.652  -0.269   6.634  30.685 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.07294    0.31097   0.235  0.81460   
x1           0.93988    0.31277   3.005  0.00272 **
x2           0.55990    0.30829   1.816  0.06965 . 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 9.83 on 997 degrees of freedom
Multiple R-squared:  0.01217,	Adjusted R-squared:  0.01018 
F-statistic: 6.139 on 2 and 997 DF,  p-value: 0.002239
\end{Soutput}
\end{Schunk}
