# Analysis of the Residuals vs Fitted

I have a model for which I gathered 10 observations from each person, a total of 25 people, then 250 observations.

Well, this is part of my summary of the model,

> summary(m)

Call:
lm(formula = fmla, data = mydata)

Residuals:
Min      1Q  Median      3Q     Max
-9.3311 -3.8480 -0.3134  3.3273 13.4413

Residual standard error: 5.246 on 216 degrees of freedom
Multiple R-squared:  0.7702,    Adjusted R-squared:  0.7351
F-statistic: 21.94 on 33 and 216 DF,  p-value: < 2.2e-16


Looking at this results may seem that the model is pretty significant, but when I plot the Residuals vs Fitted I get this plot,

Which suggests me that there is a pattern in the plot. I figure this is about the 10 observations for each person. Can anyone help me analyse this plot?

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You have repeated measures, which means you should use a mixed effects model. The pattern in your plot strongly suggests that you need to account for the repeated measures as there are 10 points on each of these "lines". – Roland Mar 11 '14 at 14:29
I never had heard of mixed effects models. I'll give it a search. Thank you. – SamuelNLP Mar 11 '14 at 14:31