# Celebrating National Book Lovers Day

August 11, 2017

On National Book Lovers Day August 9^{th}, we asked you to share your favorite books relating to statistics and data science on Facebook and Twitter. We created a list with those recommendations and added a few of our own. Whether you’re a beginner or a statistician, there are interesting reading options on the list for everyone. If you have any more book recommendations tell us!!

Stock up your bookshelves with these recommendations:

Advanced R by Hadley Wickham

Advanced R is designed for R users who want to improve their programming skills and understanding of the language. This book will teach you useful tools, techniques, and idioms that can help you become an effective R programmer.

Against the Gods: The Remarkable Story of Risk by Peter L. Bernstein

Against the Gods argues that the notion of bringing risk under control is one of the central ideas that distinguishes modern times from the distant past. It chronicles the intellectual journey that liberated humanity from prophets and fortune tellers through risk management.

Applied Statistics – Principles and Examples by D.R. Cox and E. J. Snell

This book provides an illustration of both general statistical principles and specific techniques of analysis.

Errors, Blunders, and Lies: How to Tell the Difference by David Salsburg

David Salsburg uses historical examples of errors, blunders, and lies to show how, upon closer statistical investigation, errors and blunders often lead to useful information. He also examines how statistical methods have been used to uncover falsified data.

Introduction to Probability and Mathematical Statistics by Lee J. Bain and Max Engelhardt

This book focuses on the mathematical development of probability, with examples and exercises oriented toward applications.

Linear Statistical Inference and Its Applications by C. Radhakrishna Rao

An introduction of the theory and practice of multivariate analysis using matrix algebra and the General Linear Model.

Modern Applied Statistics with S-PLUS by W. N. Venables and B. D. Ripley

This book is a guide to using S-PLUS to perform statistical analyses and provides both an introduction to the use of S-PLUS and a course in modern statistical methods.

Testing Statistical Hypotheses by Erich L. Lehmann and Joseph P. Romano

This reference for graduate students studying statistics summarizes developments in the field of hypotheses testing.

Theory of Point Estimation by Erich L. Lehmann and George Casella

This is a high-level examination of point estimation intended for those in Ph.D. programs who are interested in learning about the theory of statistics. It is a companion to Lehmann’s earlier book “Testing Statistical Hypotheses.”

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman

This book takes a statistical approach to describe ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. It could be a valuable resource for anyone interested in data mining in science or industry.

The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day by David J. Hand

In this book, renowned statistician David J. Hand argues that extraordinarily rare events are anything but. In fact, they’re commonplace.

The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century by David Salsburg

This is a book about the history of modern statistics and the role it played in the development of science and industry.

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t by Nate Silver

Nate Silver examines the world of prediction, and details the art of using probability and statistics as applied to real-world circumstances.

The Seven Pillars of Statistical Wisdom by Stephen M. Stigler

Through the lens of his seven pillars, Stephen Stigler explores the history of the seven foundational ideas of statistics—a scientific discipline related to but distinct from mathematics and computer science.

Truth or Truthiness: Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist by Howard Wainer

Howard Wainer explains how the skeptical mindset of a data scientist can expose truthiness, nonsense, and deception using the tools of causal inference.

Statistical Methods by George W. Snedecor and William G. Cochran

An extensive introduction to applied statistics emphasizing computational statistics that can be done with a calculator.

Struck by Lightning: The Curious World of Probabilities by Jeff Rosenthal

This book deconstructs the odds and oddities of chance, examining both the relevant and irreverent role of randomness in our everyday lives. A basic understanding of the rules of probability theory, applied to real-life circumstances, can help us to make sense of these situations.

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