# [R] Interpreting coefficients in linear models with interaction terms

Sun Jan 13 04:28:16 CET 2013

```Hi,

I have very limited (one class and the rest self-taught) statistics
background (I am a comparative biology major) working on an independent
study. I think that I am beginning to understand:

The coefficient SexM is the slope estimate for TestNumber1. If I add the
coefficients for the other two interaction terms to the coefficient of SexM,
I will get the slope estimate for the other two tests.

How would I quantify the significance of the interaction and SexM in the
model? If, as I have done previously and as David suggests, I look at three
different models each using only one test, I can quantify the effect of SexM
simply by looking at the associated p-value. If, however, I chose to look at
the interaction model in order to reduce the number of tests conducted , I
do not have one number to look at that quantifies the significance of sex or
the interaction. I thought about doing two F-tests, one comparing this model
to a model without interaction (to find the significance of the interaction)
and one comparing this model to one with only TestNumber (to find the total
significance of sex). When I do this, I get a p-value of 0.006 for the first
test and 0.3 for the second. My understanding of this is that SexM is
non-significant; however, the relationship between SexM and RateOfMotorPlay
significantly changes with TestNumber. This seems strange to me, but I seem
to be hearing that it is possible. If this is true, I think that reporting
that sex is non-significant is adequate and I do not need to report anything
about the interaction since my research question is related to the effect of
sex, not the change in the effect of sex over time. Does this approach
adequately address the issue of whether or not sex is related to
RateOfMotorPlay?

Thank you all so much for you helpful responces

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