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I am trying to setup a model in R. I want to test if strategies picked by different agents have a joint significant effect on the outcome.

My idea was to create a partial F-Test:

DV = success variable (0 or 1)
IV1 = agents
IV2 = strategies (around 10 types)
IV3 = agents_strategies (help variable created by me; a combination of each agent and each strategy they have chosen in the data)

L0 <- glm(DV ~ factor(`agents`), family = binomial)
L1 <- glm(DV ~ factor(`agents`) + factor(`agents_strategies`), family = binomial)
anova(L1, L0)

How can I account for multicollinearity? Can I set up my model in this way? Any help is greatly appreciated.

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    $\begingroup$ F-tests don't care about multicollinearity: you're good to go. $\endgroup$ Commented Jan 30, 2023 at 16:53

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