from zero ( -value in model adjusted for the mother' weightageand smoking status interaction we can also include interaction terms in the model let us see whether there is any interaction effect between smoke and age via the model bwt age smoke age smoke in python this can be done as follows (below we have removed some output)formula 'bwt~agesmoke bwt_model ols(formula data=bwtfit (print bwt_model summary ()ols regression results =====================================================================dep variable bwt rsquared model ols adj rsquared method least squares fstatistic no observations prob (fstatistic ) df residuals log likelihood - df model aic bic =====================================================================coef std err >| [ intercept smoke - age agesmoke - - - - we observe that the estimate for (- is significantly different from zero ( -value we therefore conclude that the effect of the mother' age on the child' weight depends on the smoking status of the mother the results on association between mother age and child weight must therefore be presented separately for the smoking and the nonsmoking group for non-smoking mothers (smoke )the mean child weight at birth increases on average by grams for each year of the mother' age this is statistically significantas can be seen from the confidence intervals for the parameters (which does not contain zero)bwt_model conf_int (intercept age smoke agesmoke - - - similarlyfor smoking mothersthere seems to be decrease in birthweightb - but this is not statistically significantsee exercise