Statistical Thinking from Scratch: A Primer for Scientists

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M. D. Edge (Postdoctoral Researcher, Postdoctoral Researcher, Department of Evolution and Ecology, University of California, Davis, USA)

Statistical Thinking from Scratch

A Primer for Scientists

M. D. Edge (Postdoctoral Researcher, Postdoctoral Researcher, Department of Evolution and Ecology, University of California, Davis, USA)

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English

Oxford University Press
21 June 2019

Mathematics & Sciences; Probability & statistics

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.

By: M. D. Edge (Postdoctoral Researcher Postdoctoral Researcher Department of Evolution and Ecology University of California Davis USA)
Imprint: Oxford University Press
Country of Publication: United Kingdom
Dimensions: Height: 253mm, Width: 194mm, Spine: 21mm
Weight: 1g
ISBN: 9780198827627
ISBN 10: 0198827628
Pages: 320
Publication Date: 21 June 2019
Audience: College/higher education , Primary
Format: Hardback
Publisher's Status: Active

Dedication Acknowledgments Prelude 1: Encountering Data 2: R and Exploratory Data Analysis 3: The Line of Best Fit 4: Probability and Random Variables 5: Properties of Random Variables Interlude 6: Properties of Point Estimators 7: Interval Estimation and Inference 8: Semiparametric Estimation and Inference 9: Parametric Estimation and Inference 10: 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

M. D. Edge is a Postdoctoral Researcher in the Department of Evolution and Ecology at the University of California, Davis. Starting in 2020, he will be an Assistant Professor of Biological Sciences in the Quantitative and Computational Biology section at the University of Southern California.

Reviews for Statistical Thinking from Scratch: A Primer for Scientists

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. * Journal of the Royal Statistical Society * Statistical Thinking from Scratch: A Primer for Scientists, a new book by M.D. Edge, a population geneticist, fills a unique niche in this landscape, sitting between the inference-focused material most biodiversity scientists are likely familiar with, and mathematical statistics books that focus on the derivations and properties of estimators.