# Topic List

```{note}
*This page is a table-of-contents organized by the topics we covered. This might be easier to navigate than the weekly sidebar.*  
```

## Topical 🛝s and Notebooks

- [What is Statistics? 🛝](/lectures/wk1/2)
- [Summarization & CLT](/lectures/wk2/03_CLT)
- [Central Tendency](/lectures/wk2/04_central_tendency)  
- [Relationships & Similarity](/lectures/wk4/01_relationships)  
- [Common Summary Measures & Formulas](/pages/formulas) 
- [Simulation & Sampling](/lectures/wk2/05_simulation)  
- [Models I: Intro & Review 🛝](/lectures/wk5/1.md)  
  - [GLM, OLS, & Linear Algebra](/lectures/wk6/01_models)
  - [Interactive Explainers](/lectures/wk6/Explainers.ipynb)
  - [Univariate Regression with `statsmodels`](/lectures/wk6/02_models_solutions)
  - [Multiple Regression with `statsmodels`](/lectures/wk6/03_models_solutions)
- [Models II: Estimation & OLS 🛝](/lectures/wk5/2.md)  
- [Models III: OLS Assumptions 🛝](/lectures/wk5/3.md)  
- [Models IV: Parameter Inference 🛝](/lectures/wk6/03.md)  
  - [Parameter Uncertainty & Inference](/lectures/wk6/04_models_solutions)
  - [Multiple regression with Categorical Predictors (2-levels)](/lectures/wk6/05_models_solutions)
  - [Interactions & Multicollinearity](/lectures/wk6/06_models_solutions)
- [Models V: Categorical Predictors & Coding Schemes 🛝](/lectures/wk7/1)  
  - [Categorical Predictors (3+ levels) & One-way ANOVA](/lectures/wk6/07_models_solutions)
- [Models VI: Categorical Predictors (3+ levels) 🛝](/lectures/wk7/2)  
- [Models VII: ANOVA review 🛝](/lectures/wk8/1)  
  - [Multiple Categorical Predictors & ANOVA](/lectures/wk6/08_models_solutions)
  - [ANOVA, contrasts, & post-hoc comparisons](/lectures/wk6/09_models_solutions)
- [Statistical Power 🛝](/lectures/wk8/2)
- [Effect Sizes 🛝](/lectures/wk9/1)
  - [Simulating Power](/lectures/wk9/02_Power-solutions)
  - [Transforming Variables](/lectures/wk9/Transformations_and_Assumptions)
- [Linear Mixed Effects Models (LMMs) 🛝](/lectures/wk10/1)
  - [Simpson's Paradox & OLS vs LMMs](/lectures/wk10/01_lmms_solutions)
  - [LMMs with Categorical Predictors & Choosing RFX](/lectures/wk10/02_lmms_solutions)

## Python centric Notebooks

(1) [Intro to Notebooks & Python](/labs/1/python)  
(2) [Python Fundamentals](/lectures/wk2/01_Python_Fundamentals_Review)  
(3) [Debugging](/lectures/wk2/02_Debugging)  
(4) [Intro to `numpy`](/labs/2/numpy)  
(5) [Intro to `matplotlib`](/labs/2/matplotlib)  
(6a) [Intro to `polars`](/labs/3/01_polars-solutions)  
(6b) [A `polars` Challenge](/labs/3/03_challenge-solutions)  
(7a) [Exploratory Data Visualization with `seaborn`](/labs/3/02_new_eda_seaborn)  
(7b) [Appendix: More `seaborn` plots](/labs/3/Appendix_01_more_seaborn_examples)