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Hi all, A small query....re. interactions concerning dummy variables and their interpretation in a model. I have 2 binary variables. One has categories "male" (1), "female" (0); the other has categories ">35 years" (1) and "<35 years" (0). Say, in a hypothetical example, we have the following data Sex age 1 0 1 1 0 0 1 1 1 1 0 1 0 0 I form an interaction like so: Sex*age 0 1 0 1 1 0 0 Now, here for the sex*age interaction a '1' is formed only when a person is 'male' and >35 years....hence we only get a contribution to the fitted value in a model from the interaction term for males over 35. How can we evaluate the contribution to a model from the individuals who are 'female and >35'; 'male and <35' and 'female and <35' when, as we can see, their contribution to the model (for the interaction)using this coding scheme is zero ? As an alternative, would it be correct to generate a categorical variable with the following codes: <35 and male 1 >35 and male 2 <35 and female 3 >35 and female 4 And then for modelling create 3 dummy variables like so: <35 and male 1 0 0 >35 and male 0 1 0 <35 and female 0 0 1 >35 and female 0 0 0 Many thanks, Kim.