No Bullshit Guide to Statistics
Index
No Bullshit Guide to Statistics
Home
Chapter 1
Chapter 1
Data Overview
Section 1.1 — Introduction to data
Section 1.2 — Data in practice
Section 1.3 — Descriptive statistics
Chapter 2
Chapter 2
Probability Theory
Section 2.1 — Discrete random variables
Section 2.2 — Multiple random variables
Section 2.3 — Inventory of discrete distributions
Section 2.4 — Calculus prerequisites
Section 2.5 — Continuous random variables
Section 2.6 — Inventory of continuous distributions
Section 2.7 — Random variable generation
Section 2.8 — Probability models for random samples
Chapter 3
Chapter 3
Inferential Statistics
Section 3.1 — Estimators
Section 3.2 — Confidence intervals
Section 3.3 — Introduction to hypothesis testing
Section 3.4 — Hypothesis testing using analytical approximations
OLD Section 3.4 — Analytical approximation methods
Section 3.5 — Two-sample hypothesis tests
Section 3.6 — Statistical design and error analysis
Section 3.7 — Inventory of statistical tests
Chapter 4
Chapter 4
Linear Models
Simple Linear Regression
Multiple Linear Regression
Interpreting Linear Models
Section 4.4 — Regression with categorical predictors
Section 4.5 — Model selection for causal inference
Section 4.6 — Generalized linear models
CH4 extra stuff
Chapter 5
Chapter 5
Bayesian Statistics
Section 5.1 — Introduction to Bayesian statistics
Section 5.2 — Bayesian inference computations
Section 5.3 — Bayesian linear models
Section 5.4 — Bayesian difference between means
Section 5.5 — Hierarchical models
Appendix
Appendix
Appendix List
Cut Material
Appendix D — Pandas tutorial
Appendix C — Python tutorial
Sampling distribution of the mean details
Estimators for categorical variables
Appendix E — Seaborn tutorial
Index
README.md