# Why must one use ANOVA to interpret the impact of interaction effects and factor variables? [closed]

What is the purpose of the ANOVA table? I once learned that you can only interpret the significance (p-value) of a multi-level discrete variable, or an interaction effect using the ANOVA table. Why? Why can't you use the p-value outputs of the regression? Why do people look at the ANOVA table in practice?

GLM

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)                          -1.9800     1.3697  -1.446    0.148
ConnectivityHIGH                      1.9214     1.6361   1.174    0.240
SusceptibilityHIGH                    0.8636     1.7183   0.503    0.615
ConnectivityHIGH:SusceptibilityHIGH  -0.6555     2.1348  -0.307    0.759


ANOVA

                            Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL                                           19     3.6712
Connectivity                 1  2.43379        18     1.2374   0.1187
Susceptibility               1  0.19710        17     1.0403   0.6571
Connectivity:Susceptibility  1  0.09598        16     0.9443   0.7567


## closed as too broad by Andy, Nick Stauner, ttnphns, Nick Cox, StatJun 1 '14 at 7:45

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

• ANOVA is regression. I don't understand your question. Maybe you're confused why the two outputs differ? Can you provide the code? – Glen May 31 '14 at 5:22
• Yes, that is exactly my question. Why are the outputs different. – blast00 May 31 '14 at 14:26
• And even moreso, under what conditions do you look at the outputs of anova vs the linear regression. i have some theories that have to do when you have mutlinomial variables but would like some more fact based knowledge. – blast00 May 31 '14 at 14:38