OpenIntro Statistics, 4th Edition
A complete foundation for Statistics, also serving as a foundation for Data Science.
Book Details
| Authors | David Diez, Mine Cetinkaya-Rundel, Christopher Barr |
| Publisher | OpenIntro |
| Published | 2019 |
| Edition | 4 |
| Paperback | 422 pages |
| Language | English |
| ISBN-13 | 9781943450077 |
| ISBN-10 | 1943450072 |
| License | Creative Commons Attribution-ShareAlike |
Book Description
OpenIntro Statistics provides a traditional college-level introduction to the field of statistics. This widely adopted textbook offers an exceptional and accessible foundation for a diverse range of students, from those at community colleges to attendees of Ivy League institutions. It is estimated that approximately 20,000 students use this thoroughly vetted textbook annually.
The book covers a first course in statistics, delivering a rigorous yet clear, concise, and accessible introduction to applied statistics. Although it was written with the undergraduate level in mind, it is also popular in advanced high school courses and certain graduate programs.
The authors intend for readers to take away three key ideas in addition to forming a foundation of statistical thinking and methods:
- Statistics is an applied field with a wide range of practical applications.
- One does not need to be a math expert to learn from real, interesting data.
- Data are messy, and statistical tools are imperfect. However, when one understands the strengths and weaknesses of these tools, they can be used to learn about the world.
The chapters of the book are structured as follows:
- Introduction to data: Data structures, variables, and basic data collection techniques.
- Summarizing data: Data summaries, graphics, and a teaser of inference using randomization.
- Probability: Basic principles of probability.
- Distributions of random variables: The normal model and other key distributions.
- Foundations for inference: General ideas for statistical inference in the context of estimating the population proportion.
- Inference for categorical data: Inference for proportions and tables using the normal and chi-square distributions.
- Inference for numerical data: Inference for one or two sample means using the t-distribution, statistical power for comparing two groups, and comparisons of many means using ANOVA.
- Introduction to linear regression: Regression for a numerical outcome with one predictor variable.
- Multiple and logistic regression: Regression for numerical and categorical data using many predictors.
This book is available under a Creative Commons Attribution-ShareAlike license (CC BY-SA), which means that you are free to copy, distribute, and modify it, as long as you credit the original author and license any derivative works under the same terms.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
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