Top Ten Statistics Books for Graduate Students

graduate statistics students
graduate statistics students

Learning Statistics – Beyond the Classroom

Are you genuinely interested in learning statistics and the all-important theories behind them? Enroll in an online statistics degree program. Master’s degree programs include books on statistics that are required or recommended by instructors – and which are handy to keep for future reference. Check out our book list, below, to supplement learning if you’re currently enrolled, or if you are looking for a refresh in various statistical areas.

The list highlights the best statistics books for graduate students and the best statistics books, in general, using recommendations based on reviews, sales, and author credentials.

The Best Books on Statistics

1. An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

Lead author Gareth James is currently the Interim Dean of the Marshall School of Business at the University of South Carolina and is recognized as an expert on statistical methodology. The book, recommended by Quartz, Good Reads, Book Scrolling, and Wall Street Mojo, includes the following:

  • Assessing model accuracy
  • An introduction to R (open source programming specifically for the social sciences)
  • Linear regression (simple and multiple)
  • Classification (logistic regression, linear discriminant analysis)
  • Resampling methods

2. Naked Statistics: Stripping the Dread from the Data by Charles Wheelan

Wheelan is a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth and a correspondent for The Economist. Wheelen states that he designed the book to apply statistical concepts to everyday life situations (e.g., how does polling work).

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

Two of the authors, Hastie and Tibshirani, co-authored An Introduction to Statistical Learning: with Applications in R. Lead author Trevor Hastie is a statistics professor at Stanford University. The book includes:

  • Supervised learning
  • Basis expansions and regularization (for non-linear relationships)
  • Kernel smoothing methods
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4. All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman

Wasserman is a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University. Recommended by both Book Scrolling and Book Authority, this book is an exhaustive view of statistical concepts. It is also the winner of the 2005 DeGroot prize (which is an honor awarded for outstanding statistical books).

5. Head First Statistics: A Brain-Friendly Guide by Dawn Griffiths

Griffiths is a mathematician and computer scientist who has written a series of “Head First” books. This series makes use of learning techniques such as visuals and activities. Reviewers note the straightforward approach to breaking down the fundamentals of statistics in lay language.

6. Principles of Statistics by MG Bulmer

Bulmer is a biostatistician and Fellow of the Royal Society of London, and an Emeritus Fellow of Wolfson College, Oxford. The original publication dates back to 1965 and remains popular. Good Reads indicates that this book remains distinctive in bridging statistical theory with practical application. The intent of this book is to enhance understanding of the concepts acquired in statistical courses.

7. Statistical Inference by George Casella, Roger L. Berger

Casella (1951-2012) was a distinguished professor in the Department of Statistics at the University of Florida. This highly recommended book breaks down the theories in statistics for increased comprehension. Intended for graduate students, it is noted as a handy reference book.

8. Statistics by David Freedman, Robert Pisani, Roger Purves

Freedman (1938-2008) was a mathematical statistician and a statistics professor at the University of California, Berkeley. This book covers such topics as:

  • Controlled experiments
  • Observational studies
  • Descriptive Statistics
  • Correlation and Regression
  • Sampling

Sampling, in particular, can be underemphasized in many texts, and it’s covered thoroughly in this one.

9. Statistics by Robert Witte, John Witte

Robert Witt, a psychology professor, taught statistics for over thirty years. John Witte is an epidemiology and biostatistics professor at the University of California, San Francisco. This particular text goes in-depth in such classical statistical procedures as:

  • Z-test
  • t-Test (one sample, independent samples, related samples)
  • Analysis of Variance (ANOVA) (One and Two Factors)
  • Chi-Square
  • Tests for Ranked (Ordinal) Data

Given the popularity of surveys with many using Likert (ordinal) scales, the section on appropriate tests for such data makes this book a must for analysts.

Last on the list of best statistics books is the primer of data visualization – another important aspect of statistics:

10. The Visual Display of Quantitative Information by Edward Tufte

Tufte is recognized as a pioneer in the field of data visualization and has been referred to as “the Da Vinci of Data.” Tufte delves into graphical practice and the theory of data graphics. Particularly noteworthy is the section entitled “chartjunk,” which goes over many common mistakes made when attempting to tell a story with data. Also included are various designs for displaying information.

Best Use of the Best Statistics Books

Most, if not all, of these books, are best used as supplements and enhancements for those enrolled in (or graduates of) advanced degree programs in statistics. Anyone interested in learning statistics should consider Michigan Technological University’s Online Masters in Statistics program. This entirely online program is particularly useful for those looking to integrate statistics and analytics into their organizations. This program is a great way to further your education and career – enjoy your reading!

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