# Module 4.3 ## Introduction Model interpretation is difficult, and comes with experience. This module will try to distill some key points and offer some advice on how to go about interpreting your model. Models only know about the world you build for them. Interpretations vary depending on model. ## Interpreting our simple log regr. model For regression models you get coefficients on your inputs. Coefficients are the contribution of that variable to the outcome assuming you already know all the other variables. this is important because the selection of other variables affects your coefficient. - Look at the coefficient of our simple model and discuss them in detail - The model will give us intercept, coeff estimates, standard error, Z and p-values. - Brief interlude on probability bayes vs frequentist. - Odds ratios - Investigate if there is collinearity - Something like here https://medium.com/analytics-vidhya/removing-multi-collinearity-for-linear-and-logistic-regression-f1fa744f3666 ## aside: when interpretation goes wrong (case studies).