[R] Interpreting coefficients in linear models with interaction terms

[theundergrad]

Hi, 

I am trying to interpret the coefficients in the model: RateOfMotorPlay ~
TestNumber + Sex + TestNumber * Sex where there are thee different tests and
Sex is (obviously) binary. My results are: Residuals:
   Min     1Q Median     3Q    Max 
-86.90 -26.28  -7.68  22.52 123.74 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)        29.430      6.248   4.710 4.80e-06 ***
TestNumber2        56.231      8.837   6.364 1.47e-09 ***
TestNumber3        75.972     10.061   7.551 1.82e-12 ***
SexM                7.101      9.845   0.721    0.472    
TestNumber2:SexM  -16.483     13.854  -1.190    0.236    
TestNumber3:SexM  -24.571     15.343  -1.601    0.111    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 40.97 on 188 degrees of freedom
Multiple R-squared: 0.3288,	Adjusted R-squared: 0.3109 
F-statistic: 18.42 on 5 and 188 DF,  p-value: 7.231e-15 

I am looking for one number that will represent the significance of the
interaction term. I was thinking of doing an F test comparing this model to
one without the interaction. When I do this, I get a highly significant
result. I am not exactly sure how to interpret this. In particular, it seems
strange to me to have a significant interaction term without both
independent variables being significant. Any advice would be highly
appreciated. 
Thanks!



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