Statistical Thinking from Scratch
A Primer for Scientists
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Product details:
- Publisher OUP Oxford
- Date of Publication 13 June 2019
- ISBN 9780198827634
- Binding Paperback
- No. of pages318 pages
- Size 246x188x16 mm
- Weight 662 g
- Language English 0
Categories
Short description:
Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.
MoreLong description:
Researchers across the natural and social sciences find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires more than the rote application of formulaic statistical methods. The premise of Statistical Thinking from Scratch is that students who want to become confident data analysts are better served by a deep introduction to a single statistical method than by a cursory overview of many methods.
In particular, this book focuses on simple linear regression-a method with close connections to the most important tools in applied statistics-using it as a detailed case study for teaching resampling-based, likelihood-based, and Bayesian approaches to statistical inference. Considering simple linear regression in depth imparts an idea of how statistical procedures are designed, a flavour for the philosophical positions one assumes when applying statistics, and tools to probe the strengths of one's statistical approach. Key to the book's novel approach is its mathematical level, which is gentler than most texts for statisticians but more rigorous than most introductory texts for non-statisticians.
Statistical Thinking from Scratch is suitable for senior undergraduate and beginning graduate students, professional researchers, and practitioners seeking to improve their understanding of statistical methods across the natural and social sciences, medicine, psychology, public health, business, and other fields.
This book is extraordinarily accessible. It is engaging, very good, and deserves wider recognition as a course text for advanced undergraduate level or beginning science research graduate students. What really makes it a compelling course (and self-learning) text are the many exercises scattered throughout. This is a very practical text whose main aim is to increase the statistical expertise of users. Throughout, the reader is treated to a lively, witty and engaging writing style. Highly recommended.
Table of Contents:
Dedication
Acknowledgments
Prelude
Encountering Data
R and Exploratory Data Analysis
The Line of Best Fit
Probability and Random Variables
Properties of Random Variables
Interlude
Properties of Point Estimators
Interval Estimation and Inference
Semiparametric Estimation and Inference
Parametric Estimation and Inference
Bayesian Estimation and Inference
Postlude: Models and Data
Appendix A: A Tour of Calculus
Appendix B: More R Details
Appendix C: Answers to Exercises
Table of mathematical notation
Glossary
Bibliography
Index
