\begin{Schunk}
\begin{Sinput}
 stPrestige <- Prestige
 stPrestige$income <- scale(stPrestige$income)
 stPrestige$education <- scale(stPrestige$education) 
 stPrestige$women <- scale(stPrestige$women)  
 stPrestige$prestige <- scale(stPrestige$prestige)
 presmod1st <- lm(prestige ~ income + education + women, data = stPrestige)
 summary(presmod1st)
\end{Sinput}
\begin{Soutput}
Call:
lm(formula = prestige ~ income + education + women, data = stPrestige)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.15229 -0.30999 -0.00793  0.29984  1.01744 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -2.822e-17  4.516e-02   0.000     1.00    
income       3.242e-01  6.855e-02   4.729 7.58e-06 ***
education    6.640e-01  6.164e-02  10.771  < 2e-16 ***
women       -1.642e-02  5.607e-02  -0.293     0.77    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4561 on 98 degrees of freedom
Multiple R-squared:  0.7982,	Adjusted R-squared:  0.792 
F-statistic: 129.2 on 3 and 98 DF,  p-value: < 2.2e-16
\end{Soutput}
\end{Schunk}
