# 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?

• 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". Mar 11, 2014 at 14:29
• I never had heard of mixed effects models. I'll give it a search. Thank you. Mar 11, 2014 at 14:31