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Our girls are real Indian girls, located all around India.They talk all Indian languages, Hindi, Tamil, Bengali, Gujarati...If the interaction effect were exactly 0 (to infinite decimal places), not only in the population, but also in your sample, the main effect betas would be the same in a model w/ or w/o the interaction [email protected], that would be better as a new question rather than information buried here in comments; you could link to this for context.Briefly, it is the mean of the reference levels when all continuous variables are =0.You can watch Cam girls doing naughty things in Video Chat just at one click.

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(The interpretation of such an interaction term is quite convoluted, but I walk through it here: Interpretation of interaction term.) d = data.frame(Sex =factor(rep(c("Male","Female"),times=2), levels=c("Male","Female")), Race =factor(rep(c("White","Black"),each=2), levels=c("White","Black")), y =c(1, 3, 5, 7)) d # Sex Race y # 1 Male White 1 # 2 Female White 3 # 3 Male Black 5 # 4 Female Black 7 is the difference between the mean of blacks and the mean of whites. I know that the p value for the interaction term is important, but does the interaction term beta have a meaningful interpretation? In the case of an interaction, the 'main effect' betas only refer to the differences within the reference level of the other factor. & it is altered from the model without interaction term due to the interaction term ameliorating the main effect?