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Version 1.3.1  (2001-08-31)

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> data(cars)
> carfit <- lm(dist ~ speed, data=cars)
> summary(carfit)

Call:
lm(formula = dist ~ speed, data = cars)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.069  -9.525  -2.272   9.215  43.201 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -17.5791     6.7584  -2.601   0.0123 *  
speed         3.9324     0.4155   9.464 1.49e-12 ***
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

Residual standard error: 15.38 on 48 degrees of freedom
Multiple R-Squared: 0.6511,	Adjusted R-squared: 0.6438 
F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12 

> plot(speed ~ dist, data=cars)
> abline(carfit)
> vcov <- summary(carfit)$sigma^2 * summary(carfit)$cov.unscaled
> carfitunres <- lm(dist ~ speed + I(speed^2), data=cars)
> carfitres <- lm(dist ~ speed, data=cars)
> anova(carfitres,carfitunres)
Analysis of Variance Table

Model 1: dist ~ speed
Model 2: dist ~ speed + I(speed^2)
  Res.Df     RSS Df Sum of Sq     F Pr(>F)
1     48 11353.5                          
2     47 10824.7  1     528.8 2.296 0.1364
> library(lmtest)
> bptest(carfit)

	Breusch-Pagan-Test

data:  form1 
BP = 4.3257, df = 2, p-value = 0.115 

> dwtest(carfit)

	Durbin-Watson-Test

data:    
DW = 1.6762, p-value = NA 

> carpred <- predict(carfit,se.fit=TRUE,interval="prediction")
> carpred$fit[1,]
      fit       lwr       upr 
 -1.84946 -34.49984  30.80092 
>