Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation book. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Introduction to Modern Statistics, 2nd Edition for free.
- Title
- Introduction to Modern Statistics, 2nd Edition
- Publisher
- OpenIntro
- Author(s)
- Johanna Hardin, Mine Çetinkaya-Rundel
- Published
- 2024-07-02
- Edition
- 2
- Format
- eBook (pdf, epub, mobi)
- Pages
- 510
- Language
- English
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Preface I Introduction to data 1 Hello data 2 Study design 3 Applications: Data II Exploratory data analysis 4 Exploring categorical data 5 Exploring numerical data 6 Applications: Explore III Regression modeling 7 Linear regression with a single predictor 8 Linear regression with multiple predictors 9 Logistic regression 10 Applications: Model IV Foundations of inference 11 Hypothesis testing with randomization 12 Confidence intervals with bootstrapping 13 Inference with mathematical models 14 Decision Errors 15 Applications: Foundations V Statistical inference 16 Inference for a single proportion 17 Inference for comparing two proportions 18 Inference for two-way tables 19 Inference for a single mean 20 Inference for comparing two independent means 21 Inference for comparing paired means 22 Inference for comparing many means 23 Applications: Infer VI Inferential modeling 24 Inference for linear regression with a single predictor 25 Inference for linear regression with multiple predictors 26 Inference for logistic regression 27 Applications: Model and infer Appendices A Exercise solutions B References
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