\begin{Schunk}
\begin{Sinput}
 m1mc <- meanCenter(m1)
 summary(m1mc)
\end{Sinput}
\begin{Soutput}
These variables were mean-centered before any transformations were made on the design matrix.
[1] "educationc"
The centers and scale factors were 
      educationc
mean     10.7951
scale     1.0000
The summary statistics of the variables in the design matrix (after centering). 
                        mean std.dev.
prestige            47.32755 17.09491
educationc           0.00000  2.74894
typeprof             0.31633  0.46743
typewc               0.23469  0.42599
educationc:typeprof  1.04043  1.72183
educationc:typewc    0.05319  0.45019

The following results were produced from: 
meanCenter.default(model = m1)

Call:
lm(formula = prestige ~ educationc * type, data = stddat)

Residuals:
     Min       1Q   Median       3Q      Max 
-19.7095  -5.3938   0.8125   5.3968  16.1411 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)          47.1305     2.7609  17.071  < 2e-16 ***
educationc            4.7637     1.0247   4.649 1.11e-05 ***
typeprof              8.2758     4.5791   1.807   0.0740 .  
typewc               -6.3453     3.2333  -1.962   0.0527 .  
educationc:typeprof  -0.9808     1.4495  -0.677   0.5003    
educationc:typewc     1.6709     2.0777   0.804   0.4233    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.827 on 92 degrees of freedom
Multiple R-squared:  0.8012,	Adjusted R-squared:  0.7904 
F-statistic: 74.14 on 5 and 92 DF,  p-value: < 2.2e-16
\end{Soutput}
\end{Schunk}
