Library Catalogue

Image from Google Jackets

Statistical Inference For Everyone Brian Blais

By: Contributor(s): Material type: TextTextSeries: Open textbook libraryDistributor: Minneapolis, MN Open Textbook LibraryPublisher: Smithfield, Rhode Island Brian Blais [2017]Copyright date: ©2017Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
Subject(s): LOC classification:
  • QA1
  • QA37.3
  • QA273-280
Online resources:
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
Subject: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

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

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.

Attribution-ShareAlike

In English.

Description based on online resource

There are no comments on this title.

to post a comment.

© 2024, Kenya Medical Training College | All Rights Reserved