Statistical Inference For Everyone Brian Blais
Material type:
TextSeries: Open textbook libraryDistributor: Open Textbook Library Publisher: Brian Blais Description: 1 online resourceSubject(s): Mathematics -- Textbooks | Applied mathematics -- TextbooksLOC classification: QA1QA37.3Online resources: Access online version | Item type | Current library | Call number | URL | Status | Date due | Barcode | Item holds |
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eBook
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Online Access | QA1 (Browse shelf(Opens below)) | Link to resource | Available |
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.
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In English.
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