\begin{Schunk}
\begin{Sinput}
 presmod3st <- standardize(presmod2)
 summary(presmod3st)
\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
typeprofs     0        1
typewcs       0        1

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.86274 -0.26217  0.01824  0.30698  1.08206 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -2.318e-16  4.214e-02   0.000 1.000000    
incomes      2.579e-01  6.487e-02   3.976 0.000139 ***
educations   5.889e-01  1.039e-01   5.671 1.63e-07 ***
womens       1.183e-02  5.577e-02   0.212 0.832494    
typeprofs    1.615e-01  1.077e-01   1.500 0.137127    
typewcs     -7.269e-02  6.642e-02  -1.094 0.276626    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4172 on 92 degrees of freedom
Multiple R-squared:  0.8349,	Adjusted R-squared:  0.826 
F-statistic: 93.07 on 5 and 92 DF,  p-value: < 2.2e-16
\end{Soutput}
\end{Schunk}
