\begin{Schunk}
\begin{Sinput}
 pres1st <- standardize(presmod1)
 summary(pres1st)
\end{Sinput}
\begin{Soutput}
All variables in the model matrix and the dependent variable
were centered. The centered variables have the letter "s" appended to their
non-centered counterparts, even constructed
variables like `x1:x2` and poly(x1,2). We agree, that's probably
ill-advised, but you asked for it by running standardize().

The rockchalk function meanCenter is a smarter option, probably. 

The summary statistics of the variables in the design matrix. 
           mean std.dev.
prestiges     0        1
incomes       0        1
educations    0        1
womens        0        1

Call:
lm(formula = prestiges ~ incomes + educations + womens, data = stddat)

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    
incomes      3.242e-01  6.855e-02   4.729 7.58e-06 ***
educations   6.640e-01  6.164e-02  10.771  < 2e-16 ***
womens      -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}
