Hello R Gurus:

I'm doing a simple linear regression model:

modelo1 <- lm(X9 ~ 1 + X1 + I(log(X2)) + X3 + I(log(X4)) + X5 + I(log(X6)) + X7)

of which i later do a plot:

plot(modelo1)

This shows 4 graphics, about which I ask:

1) In the "Residuals vs. Fitted", what does the red curve represent?
2) What does the "scale-location" graphic show? How is it different from the "residuals vs. fitted? (I mean, changing the scale of the Y axis to show standarized residuals does not look like a big difference to me) What does the red curve represent in that graphic?
3) How do i interpret the whole "residuals vs. leverage" graphic and what is that "cook's distance" business about?

I'm basically intereseted in doing a residual analysis (you know, at least "visually" confirming the conditions of the gauss markov hypothesis).   I understand that the QQplot allows me to visually confirm if the residuals are normally distributed, but how do i use the other graphics to verify homocedasticity and independance of the residuals from the model variables?

Thanks in advance,

jose loreto romero




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