📊 Statistics
Based on OpenIntro Statistics, licensed CC BY-SA 3.0.
Data analysis and inference, translated into runnable Python with diagrams.
| Chapter | |||
|---|---|---|---|
| 1. | Introduction to Data | Observations, variables, study design, and sampling strategies | 📊 |
| 2. | Summarizing Data | Histograms, box plots, measures of center and spread, contingency tables | 📊 |
| 3. | Probability | Rules, conditional probability, Bayes' theorem, independence | 📊 |
| 4. | Distributions | Normal, binomial, Poisson, and geometric distributions with Z-scores | 📊 |
| 5. | Foundations for Inference | Point estimates, confidence intervals, hypothesis testing, p-values | 📊 |
📺 Video lectures: StatQuest with Josh Starmer
Neighbors
- 🎰 Probability — statistics applies probability theory to data
- 🔬 Scientific Method — p-values, significance, and replication live here
- 🤖 Machine Learning — regression and classification as statistical inference
- 📡 Information Theory — KL divergence appears in both fields