Update statistical_modelling.md

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@@ -29,11 +29,11 @@ In this workshop we focus on practical data analysis by presenting statistical w
 
 ## Linear Models 3: How to build interpretable models and analyse data to extract insightful & impactful patterns, and craft an engaging research story
 Statistical analysis is more than just building the best predictive model, it should also enable you to make impactful discoveries that expand our knowledge. Constructing engaging narratives about your research is also invaluable as you look to connect with your field, the community and funding bodies. To do this you need to build interpretable models, test hypotheses, uncover insightful & impactful patterns, and present results in insightful, intuitive and memorable ways. In this workshop we explore tips and tricks to make your research do just that. Topics covered will be:
-  - **Building impactful real-world recommendations and guidelines** – i) why we need to understand both stated and model derived importance, ii) how Quadrant Analysis uses both variable performance and importance to develop impactful real-world recommendations and guidelines.
-  - **Reporting tricks that extract insightful & impactful patterns and craft engaging stories** – i) establishing the importance of a predictor/risk factor, ii) confidence vs prediction intervals, iii) applying and correcting for multiple comparisons, iv) testing different hypothesis using different model parameterisations of the design matrix, v) interpreting categorical predictors - dummy vs effects coding and estimated marginal means, plus other reporting and interpretation tricks.
-  - **Building interpretable models** – it’s quite common for researchers to incorrectly use model parameters to establish variables ‘impact’ or ‘importance’ . We show how multi-collinearity prevents this interpretation, and how to assess and then fix it so parameters can be used to identify important predictor/risk factors and other insightful patterns.
-  - **Mixed models** – extend the Linear Model 1 intro to: i) better explain how mixed models work, ii) use them to test population wide hypotheses outside your sampled groups, iii) use a random slope (with examples of the patterns it can explain and hypotheses it can test).
-  - **Using data visualisation to report complex nonlinear models graphically and aid pattern extraction**
+  **Building impactful real-world recommendations and guidelines** – i) why we need to understand both stated and model derived importance, ii) how Quadrant Analysis uses both variable performance and importance to develop impactful real-world recommendations and guidelines.
+  **Reporting tricks that extract insightful & impactful patterns and craft engaging stories** – i) establishing the importance of a predictor/risk factor, ii) confidence vs prediction intervals, iii) applying and correcting for multiple comparisons, iv) testing different hypothesis using different model parameterisations of the design matrix, v) interpreting categorical predictors - dummy vs effects coding and estimated marginal means, plus other reporting and interpretation tricks.
+  **Building interpretable models** – it’s quite common for researchers to incorrectly use model parameters to establish variables ‘impact’ or ‘importance’ . We show how multi-collinearity prevents this interpretation, and how to assess and then fix it so parameters can be used to identify important predictor/risk factors and other insightful patterns.
+  **Mixed models** – extend the Linear Model 1 intro to: i) better explain how mixed models work, ii) use them to test population wide hypotheses outside your sampled groups, iii) use a random slope (with examples of the patterns it can explain and hypotheses it can test).
+  **Using data visualisation to report complex nonlinear models graphically and aid pattern extraction**
   * [Linear Models 3 v1.51](assets/files/Linear%20Models%20III%20Model%20building%20tips%2C%20extracting%20patterns%2C%20crafting%20engaging%20stories%20HANDOUTS%20v1.51%2022-7-2024.pdf)