Statistical Inference For Everyone

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Brian Blais, Brown University

Pub Date: 2017

ISBN 13:

Publisher: Independent

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Table of Contents

Proposal 

  • 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

Bibliography
Appendix A: Computational Analysis
Appendix B: Notation and Standards 
Appendix C: Common Distributions and Their Properties 
Appendix D: Tables 

About the Book

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.

About the Contributors

Author(s)

Brian Blais professor of Science and Technology, Bryant University and a research professor at the Institute for Brain and Neural Systems, Brown University.