Module 4.3: Interpreting a model

[callummole]

Section 3 - Interpreting a model

Description

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. 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. You can't interpret coefficients in splendid isolation
  • parameter uncertainty + errors.
  • Brief interlude on probability bayes vs frequentist
• Visualisation helps you understand what the model is telling you. Ask yourself, what does the model think is going on in your data.
  • Explicitly test hypotheses. (can we do inference here? Or do we need uncertainty/residuals from the next section?).
  • prediction/simulation.
• aside: when interpretation goes wrong (case studies).

Estimation

Again. Intuitive figures will be key, and there will be a lot of graphing of predictions and different relationships. 8 hours

[crangelsmith]

Draft hackmd https://hackmd.io/a0CStccXS3KD6DYPigjDjQ