Hi.
I apologize if this is no a question I should post here. I am trying to analyze the interaction and confusion between variables in order to create a multiple linear regression model using STATA 15.
Dependent variable: HDL cholesterol (mg / dL) - quantitative variable (colhdl)
Independent variable: alcohol consumption - qualitative variable / 3 categories (1 “non-drinker”, 2 “moderate drinker” and 3 “risk drinker”).
1) I do not know if the variables should be added to the model as it is analyzed if there are interactions or if they appear to be confounding variables, but what if they are not or if only one category of an analyzed variable is significant?
For example:
- regress colhdl i.drinker -> gender: binary variable (1: male, 2: female)
- regress colhdl i.drinker if gender == 1
- regress colhdl i.drinker if gender == 2
- regress colhdl i.gender ## i.drinker
----------stata:
----------------------Coef. Std. Err. t P>|t| [95% Conf. Interval]
female #
mod drinker | .7557405 1.212174 0.62 (0.533) -1.621379 3.13286
female #
risk drinker | 3.273935 1.678158 1.95 (0.051) -.0169984 6.564868
_cons | 44,57028 .778798 57.23 (0.000) 43.04303 46.09753
*p value in parenthesis.
If it is not significant, do I not add this variable to the model? Or should I assess whether it is a confounding variable?
What do I do if only one of the dummies is significant? How do I add it to the model if I have other variables?
Thank you so much.
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