\begin{Schunk}
\begin{Sinput}
 x1 <- rnorm(100); x2 <- -4 + rpois(100,lambda=4);
 y = 0.1 * x1 + 0.2 *x2 + rnorm(100);
 dat <- data.frame(x1, x2, y); rm (x1, x2, y)
 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 
-2.29298 -0.55317  0.02582  0.56164  2.73118 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.05245    0.09697   0.541   0.5898    
x1           0.20560    0.08538   2.408   0.0179 *  
x2           0.20855    0.04473   4.662    1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.942 on 97 degrees of freedom
Multiple R-squared:  0.2082,	Adjusted R-squared:  0.1919 
F-statistic: 12.76 on 2 and 97 DF,  p-value: 1.207e-05
\end{Soutput}
\end{Schunk}
