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? 🛝

• Summarization & CLT

• Central Tendency

• Relationships & Similarity

• Common Summary Measures & Formulas

• Simulation & Sampling

• Models I: Intro & Review 🛝

  • GLM, OLS, & Linear Algebra

  • Interactive Explainers

  • Univariate Regression with statsmodels

  • Multiple Regression with statsmodels

• Models II: Estimation & OLS 🛝

• Models III: OLS Assumptions 🛝

• Models IV: Parameter Inference 🛝

  • Parameter Uncertainty & Inference

  • Multiple regression with Categorical Predictors (2-levels)

  • Interactions & Multicollinearity

• Models V: Categorical Predictors & Coding Schemes 🛝

  • Categorical Predictors (3+ levels) & One-way ANOVA

• Models VI: Categorical Predictors (3+ levels) 🛝

• Models VII: ANOVA review 🛝

  • Multiple Categorical Predictors & ANOVA

  • ANOVA, contrasts, & post-hoc comparisons

• Statistical Power 🛝

• Effect Sizes 🛝

  • Simulating Power

  • Transforming Variables

• Linear Mixed Effects Models (LMMs) 🛝

  • Simpson’s Paradox & OLS vs LMMs

  • LMMs with Categorical Predictors & Choosing RFX

Python centric Notebooks

(1) Intro to Notebooks & Python
(2) Python Fundamentals
(3) Debugging
(4) Intro to numpy
(5) Intro to matplotlib
(6a) Intro to polars
(6b) A polars Challenge
(7a) Exploratory Data Visualization with seaborn
(7b) Appendix: More seaborn plots