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    Statistical Inference For Everyone

    (3 reviews)

    Brian Blais, Bryant University

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    Publisher: Brian Blais

    Language: English

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    CC BY-SA


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    Reviewed by Kenese Io, PhD candidate, Colorado State University on 11/30/20

    The book illustrates a very pragmatic approach with little theoretical application. I would recommend this text to anyone who is teaching applied stats at an early level. read more

    Reviewed by Jimmy Chen, Assistant Professor, Bucknell University on 1/26/19

    As far as Statistical Inference goes, the author has done a great job covering the essential topics. The breadth and the depth of the content are are well balanced. I believe this book can be a great supplemental material for any statistics or... read more

    Reviewed by Adam Molnar, Assistant Professor, Oklahoma State University on 5/21/18

    This book is not a comprehensive introduction to elementary statistics, or even statistical inference, as the author Brian Blais deliberately chose not to cover all topics of statistical inference. For example, the term matched pairs never... read more

    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

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

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    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


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

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