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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