Statistical Inference For EveryoneThis is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.
Table of Contents
1 Introduction to Probability
2 Applications of Probability
3 Random Sequences and Visualization
4 Introduction to Model Comparison
5 Applications of Model Comparison
6 Introduction to Parameter Estimation
7 Priors, Likelihoods, and Posteriors
8 Common Statistical Significance Tests
9 Applications of Parameter Estimation and Inference
10 Multi-parameter Models
11 Introduction to MCMC
12 Concluding Thoughts