Hi Paul,

here's a lm model to illustrate this:


 > summary(lm(y~x.1+x.2))

Call:
lm(formula = y ~ x.1 + x.2)

Residuals:
       Min         1Q     Median         3Q        Max
-0.0561359 -0.0054020  0.0004553  0.0056516  0.0515817

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.0007941  0.0002900   2.738 0.006278 **
x.1         -0.0446746  0.0303192  -1.473 0.140901    
x.2          0.1014467  0.0285513   3.553 0.000396 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.009774 on 1134 degrees of freedom
  (64 observations deleted due to missingness)
Multiple R-Squared: 0.01336,    Adjusted R-squared: 0.01162
F-statistic: 7.676 on 2 and 1134 DF,  p-value: 0.0004883



summary(lm(...)) computes t-values and the resulting p-values for each 
regressor.
The intercept is significant at 0.6%, similarly, x.2 is significant at 
0.04%. Only x.1 is not significant at a conventional level of 5%. Its p 
is 14%.

Overall significance of the model is given by the F stats (=7.676 at p 
less than 0.05%).

Hope that helped.

Bernd

	[[alternative HTML version deleted]]

