Statistical Inference For Everyone

Format:

Book

Author/Creator:

Blais, Brian, author.

Language:

English

Subjects (All):

Mathematics--Textbooks.

Mathematics.

Applied mathematics--Textbooks.

Applied mathematics.

Physical Description:

1 online resource

Place of Publication:

Smithfield, Rhode Island Brian Blais [2017]

Language Note:

In English.

Summary:

This 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.

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

BibliographyAppendix A: Computational AnalysisAppendix B: Notation and StandardsAppendix C: Common Distributions and Their PropertiesAppendix D: Tables

Notes:

Description based on online resource