Charles Wheelan is the best-selling author of Naked Statistics and Naked Economics and a former Economist correspondent. He is a Dartmouth College professor of public policy and economics.

Statistics, once thought to be tedious, is rapidly evolving into a discipline that Hal Varian, Google's chief economist, has actually called "sexy." The real-world application of statistics continues to expand by leaps and bounds, from batting averages and political polls to game shows and medical research. How can we catch schools that use standardized tests to their advantage? How does Netflix know which films you'll enjoy? What is causing the increase in autism cases? As best-selling author Charles Wheelan demonstrates in Naked Statistics, the right data and a few well-chosen statistical tools can assist us in answering these and other questions.

This book is a lifesaver for those who slept through Stats 101. Wheelan cuts through the jargon and technical jargon to get to the underlying intuition that drives statistical analysis. He explains key concepts like inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and demonstrates how brilliant and creative researchers are using valuable data from natural experiments to answer difficult questions.

And there isn't a dull page in sight, in Wheelan's trademark style. You'll come across clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a mind-boggling choice from the popular game show Let's Make a Deal, and you'll walk away with new insights each time. Wheelan defies the odds once more by bringing to life another essential, previously unglamorous discipline with the wit, accessibility, and sheer fun that made Naked Economics a bestseller.

Author: Charles Wheelan

Link to buy: https://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/039334777X

Ratings: 4.6 out of 5 stars (from 2318 reviews)

Best Sellers Rank: #14,102 in Books

#5 in Business Statistics

#11 in Statistics (Books)

#17 in Probability & Statistics (Books)

Stanford University statistics professors Trevor Hastie, Robert Tibshirani, and Jerome Friedman They are well-known researchers in this field: Hastie and Tibshirani created generalized additive models and wrote a popular book with the same name. Hastie invented principal curves and surfaces and co-developed much of the statistical modeling software and environment in R/S-PLUS. Tibshirani invented the lasso and is co-author of the best-selling An Introduction to Bootstrap. Friedman co-invented numerous data-mining tools, including CART, MARS, projection pursuit, and gradient boosting.

In a common conceptual framework, The Elements of Statistical Learning describes important ideas in a variety of fields such as medicine, biology, finance, and marketing. Despite the statistical approach, the emphasis is on concepts rather than mathematics. Many examples are provided, with extensive use of color graphics. It's an excellent resource for statisticians and anyone else interested in data mining in science or industry. The book covers a wide range of topics, from supervised learning (prediction) to unsupervised learning. Among the numerous topics covered are neural networks, support vector machines, classification trees, and boosting, which is the first comprehensive treatment of this topic in any book.

Many topics not covered in the original are covered in this major new edition, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. A chapter on methods for "wide" data (p greater than n), including multiple testing and false discovery rates, is also included.

Author: Trevor Hastie, Robert Tibshirani and Jerome Friedman

Link to buy: https://www.amazon.com/gp/product/0387848576/

Ratings: 4.6 out of 5 stars (from 4025 reviews)

Best Sellers Rank: #19,781 in Books

#8 in Data Mining (Books)

#13 in Artificial Intelligence & Semantics

#24 in Probability & Statistics (Books)

Stanford University is where Robert Witte received his Ph.D. He is an Emeritus Professor of Psychology at San Jose State University, where he taught statistics courses for over three decades. He has a number of peer-reviewed journal publications, as well as several nationally competitive research grants and a post-doctoral Research Fellowship at Indiana University.

John Witte is an Epidemiology and Biostatistics Professor at the University of California, San Francisco. He earned his doctorate from the University of California, Los Angeles, and has previously taught at the University of Southern California and Case Western Reserve University. He has over 150 papers to his credit, and his research focuses primarily on statistical genetics and the genetic epidemiology of cancer.

Statistics, 11th Edition authors draw on over 40 years of experience to provide business professionals with a clear and methodical approach to essential statistical procedures. The text explains the fundamental concepts and procedures of descriptive and inferential statistical analysis in detail. It emphasizes expressions involving sums of squares and degrees of freedom, as well as the significance of variability. This approachable approach will assist business professionals in tackling perennially perplexing topics such as standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and effect size estimates.

Author: Robert Witte and John Witte

Link to buy: https://www.amazon.com/gp/aw/d/B01NBNDBE2/

Ratings: 4.6 out of 5 stars (from 101 reviews)

Best Sellers Rank: #482,008 in Kindle Store

#16 in Psychology Statistics

#593 in Statistics (Books)

Dawn Griffiths began her career as a mathematician at a prestigious UK university. She received a First-Class Honours degree in Mathematics and was awarded a university scholarship to pursue a PhD in differential equations of particularly rare breeds. She left academia after realizing that people would stop talking to her at parties, and instead pursued a career in software development. She currently juggles IT consulting, writing, and mathematics.

Wouldn't it be great if there was a statistics book that made histograms, probability distributions, and chi square analysis as fun as going to the dentist? Head First Statistics brings statistics to life with puzzles, stories, quizzes, visual aids, and real-world examples.

Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula will help you grasp key concepts and put them to use. Learn how to visually present data with charts and plots; understand the difference between taking the average with mean, median, and mode and why it matters; calculate probability and expectation; and much more.

Among the best books on statistics, Head First Statistics is ideal for high school and college statistics students, and it meets the College Board's Advanced Placement (AP) Statistics Exam requirements. You'll learn how to:

• Examine the entire scope of first-year statistics topics.
• Take on difficult statistical concepts with Head First's dynamic, visually rich format, which has been shown to stimulate learning and help you retain knowledge.
• Investigate real-world scenarios ranging from casino gambling to prescription drug testing in order to put statistical principles into context.
• Learn how to calculate odds, measure spread, and understand the normal, binomial, geometric, and Poisson distributions.
• Perform sampling, correlation and regression analysis, hypothesis testing, chi square analysis, and other tasks.

You'll not only have mastered statistics before you know it, but you'll also understand how they work in practice. Head First Statistics will help you pass your statistics course while also providing you with a solid understanding of the subject that you can apply throughout your life.

Author: Dawn Griffiths

Link to buy: https://www.amazon.com/Head-First-Statistics-Brain-Friendly-Guide/dp/0596527586/

Ratings: 4.4 out of 5 stars (from 208 reviews)

Best Sellers Rank: #559,615 in Books

#234 in Statistics (Books)

#896 in Probability & Statistics (Books)

Allen Downey is a computer science associate professor at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College, and the University of California, Berkeley. He holds a Ph.D. in Computer Science from the University of California, Berkeley, as well as Master's and Bachelor's degrees from MIT.

If you know how to program, you can use probability and statistics tools to convert data into knowledge. This concise introduction demonstrates how to perform statistical analysis computationally, rather than mathematically, using Python programs.

You'll learn the entire process of exploratory data analysis by working with a single case study throughout Think Stats, from collecting data and generating statistics to identifying patterns and testing hypotheses. You'll learn about distributions, probability rules, visualization, and a variety of other tools and concepts.

Your discoveries will be enriched by new chapters on regression, time series analysis, survival analysis, and analytic methods.

• Write and test code to gain an understanding of probability and statistics.
• Experiment with statistical behavior by generating samples from various distributions.
• Use simulations to help you understand concepts that are difficult to grasp mathematically.
• Python can import data from most sources rather than relying on data that has been cleaned and formatted for statistics tools.
• Answer questions about real-world data using statistical inference.

Author: Allen B. Downey

Link to buy: https://www.amazon.com/Think-Stats-Exploratory-Data-Analysis/dp/1491907339

Ratings: 4.3 out of 5 stars (from 136 reviews)

Best Sellers Rank: #293,004 in Books

#119 in Data Modeling & Design (Books)

#330 in Python Programming

#410 in Probability & Statistics (Books)

The Ohio State University's Deborah J. Rumsey, PhD, is a Professor of Statistics and a Statistics Education Specialist. Statistics Workbook For Dummies, Statistics II For Dummies, and Probability For Dummies are her famous books.

Statistics got you down? Is there no fear? This user-friendly guide provides clear, practical explanations of statistical ideas, techniques, formulas, and calculations, as well as numerous examples that demonstrate how these concepts apply in everyday life.

Among the best books on statistics, Statistics For Dummies teaches you how to interpret and critique graphs and charts, calculate the odds using probability, guess with confidence using confidence intervals, set up and run a hypothesis test, compute statistical formulas, and much more.

• Tracks that correspond to a typical first semester statistics course
• Updated examples are appealing to today's students.
• Explanations are consistent with teaching methods and classroom protocol.

Statistics For Dummies is jam-packed with practical advice and real-world problems that will teach you how to analyze and interpret data for better classroom or on-the-job performance.

Author: Deborah J. Rumsey

Link to buy: https://www.amazon.com/Statistics-Dummies-Math-Science/dp/1119293529

Ratings: 4.3 out of 5 stars (from 1704 reviews)

Best Sellers Rank: #7,526 in Books

#3 in Statistics (Books)

#7 in Probability & Statistics (Books)

#11 in Math Teaching Materials

Larry Wasserman is a statistics professor at Carnegie Mellon University. He is also a member of the School of Computer Science's Center for Automated Learning and Discovery. Nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics are among his research interests. He received the Committee of Presidents of Statistical Societies Presidents' Award in 1999 and the Centre de recherches mathematiques de Montréal-Statistical Society of Canada Prize in Statistics in 2002. He is a member of the editorial boards of The Journal of the American Statistical Association and The Annals of Statistics. He is an American Statistical Association and Institute of Mathematical Statistics fellow.

The title "All of Statistics" is an exaggeration if taken literally. However, the title is appropriate in spirit, as the book covers a much broader range of topics than a typical introductory book on mathematical statistics. This book is intended for people who want to quickly learn probability and statistics. It is appropriate for graduate or advanced undergraduate students studying computer science, mathematics, statistics, and other related fields.

Modern topics covered in the book include non-parametric curve estimation, bootstrapping, and classification, which are typically reserved for advanced courses. The reader is assumed to be familiar with calculus and a little linear algebra. There is no prior knowledge of probability or statistics required. Data collection and analysis are central to statistics, data mining, and machine learning.

Author: Larry Wasserman

Link to buy: https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721

Ratings: 4.5 out of 5 stars (from 171 reviews)

Best Sellers Rank: #618,673 in Books

#187 in Mathematical & Statistical Software

#261 in Mathematical Physics (Books)

#1,027 in Probability & Statistics (Books)

Timothy C. Urdan is an Associate Professor of Psychology at Santa Clara University. In 1994, he earned his Ph.D. in Education and Psychology from the University of Michigan, where he also received several honors, including the School of Education Merit Award, the Horace H. Rackham Predoctoral Fellowship, and the Burke Aaron Hinsdale Scholar. He has been a member of the editorial boards of Contemporary Educational Psychology and the Journal of Educational Psychology since 2001.

This low-cost paperback provides a brief, straightforward overview of statistics to help readers gain a better understanding of how statistics work and how to correctly interpret them. Each chapter describes a different statistical technique, ranging from fundamental concepts like central tendency and distribution description to more advanced concepts like t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a brief overview of the statistic and when it should be applied. Following that is a more detailed explanation of how the statistic works. Finally, each chapter concludes with an example of the statistic in use and a sample of how the results of statistical analyses might be written up for publication. There is also a glossary of statistical terms and symbols.

The third edition of Statistics in Plain English includes the following new features:

• a new chapter on Factor and Reliability Analysis, which will be especially useful to those who conduct and/or read survey research
• new "Writing it Up" sections that demonstrate how to write about and interpret statistics seen in books and journals
• a website at http://www.psypress.com/statistics-in-plain-english/ with PowerPoint presentations, interactive problems (including an overview of the problem's solution for Instructors)
• an IBM SPSS dataset for practice, and videos of the author
• new section on understanding data distribution to help readers understand how to use and interpret graphs
• many more examples, tables, and charts to help students visualize key concepts

Statistics in Plain English, Third Edition is an excellent supplement for statistics, research methods, and/or statistics-related courses taught at the undergraduate or graduate level, as well as a reference tool for anyone looking to refresh their memory on key statistical concepts. Examples of research come from psychology, education, and other social and behavioral sciences.

Author: Timothy C. Urdan

Link to buy: https://www.amazon.com/dp/041587291X

Ratings: 4.4 out of 5 stars (from 134 reviews)

Best Sellers Rank: #1,078,553 in Books

#472 in Medical Psychology Research

#1,141 in Statistics (Books)

#2,067 in Probability & Statistics (Books)

Alex Reinhart is a Carnegie Mellon University statistics instructor and Ph.D. candidate. He earned a BS in physics from the University of Texas at Austin and conducts research on radioactive device detection using statistics and physics.

Scientific progress is dependent on good research, which requires good statistics. But statistical analysis is difficult to master, even for the brightest among us. It's surprising how many scientists get it wrong.

Statistics Done Wrong is a concise, essential guide to statistical blunders in modern science that will teach you how to avoid making mistakes in your research. You'll look at embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these errors to occur, and start your journey to change the way you and your peers do statistics. The book is regarded as one of the best books on statistics.

You can get advice on:

• Asking the appropriate question, designing the appropriate experiment, selecting the appropriate statistical analysis, and sticking to the plan
• What are p values, significance, insignificance, confidence intervals, and regression?
• Selecting the appropriate sample size and avoiding false positives
• Reporting your findings and making your data and source code available
• Procedures to be followed, precautions to be taken, and analytical software that can assist

Author: Alex Reinhart

Link to buy: https://www.amazon.com/dp/1593276206

Ratings: 4.5 out of 5 stars (from 303 reviews)

Best Sellers Rank: #126,940 in Books

#43 in Business Statistics

#98 in Statistics (Books)

#164 in Probability & Statistics (Books)

Gareth James is a data sciences and operations professor at the University of Southern California. He has a large body of methodological work in the domain of statistical learning, with a focus on high-dimensional and functional data. His MBA elective courses in this area inspired the conceptual framework for this book.

Daniela Witten is a statistics and biostatistics associate professor at the University of Washington. Her research is primarily concerned with statistical machine learning in the high-dimensional setting, with a particular emphasis on unsupervised learning.

Trevor Hastie and Robert Tibshirani are Stanford University statistics professors and co-authors of the popular textbook Elements of Statistical Learning. Hastie and Tibshirani created generalized additive models and published a popular book with the same title.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, which is an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics over the last two decades. This book discusses some of the most important modeling and prediction techniques, as well as their applications. Linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and other topics are covered. The methods presented are illustrated with color graphics and real-world examples. Because the goal of this textbook is to make it easier for practitioners in science, industry, and other fields to use these statistical learning techniques, each chapter includes a tutorial on how to implement the analyses and methods presented in R, an extremely popular open source statistical software platform.

The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers, was co-written by two of the authors. An Introduction to Statistical Learning covers many of the same topics, but at a much more accessible level. The book is also among the best books on statistics.

This book is intended for both statisticians and non-statisticians who want to analyze their data using cutting-edge statistical learning techniques. The text assumes only a basic understanding of linear regression and no prior knowledge of matrix algebra.

Author: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Link to buy: https://www.amazon.com/dp/1461471370

Ratings: 4.7 out of 5 stars (from 1793 reviews)

Best Sellers Rank: #88,313 in Books

#14 in Mathematical & Statistical Software

#92 in Artificial Intelligence & Semantics

#112 in Probability & Statistics (Books)