Dear Gustaf,
I can think of two reasons why the two tests can disagree.
First, the t-test from the summary() output is based on the covariance
matrix of the coefficients, while the F-test in the anova() output is
based of fitting alternative models. The two are not in general the
same.
Second,
Hi,
I have been struggling with this problem for some time now. Internet,
books haven't been able to help me.
## I have factorial design with counts (fruits) as response variable.
> str(stubb)
'data.frame': 334 obs. of 5 variables:
$ id : int 6 23 24 25 26 27 28 29 31 34 ...
$ infl.treat : Facto
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