When selecting statistics textbooks for college courses, instructors have much to consider. Statistical courses occur across disciplines, and students and instructors have differing backgrounds, capabilities, and goals. Choosing the right textbook that meets these criteria when inundated with the milieu of options available can be daunting. This guide of popular statistics textbooks with a corresponding listing of the major universities that use them can help instructors find the correct statistics textbooks for their classes.

Best Beginner Statistics Textbooks: Applied Statistics

Courses that explore statistics for students without advanced mathematical backgrounds can start with Statistics: A Very Short Introduction by D.J. Hand instead of focusing on statistical concepts. This primer discusses the importance and misuse of statistics in everyday life. Professors in applied statistics at USC, Stanford, Wash U, University of Chicago and MIT also use Statistics, by David Freedman, Robert Pisani and Roger Purves to explore statistical concepts most prevalent in research. The Art of Statistics: How to Learn from Data by David Spiegelhalter has a very accessible format for applied researchers but remains comprehensive in its range of statistical topics. For applied statistics instructors looking for real-world experiences for students, Introduction to the Practice of Statistics by David Moore, George McCabe and Bruce Craig includes data analysis with real datasets. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie et al. has a large breadth of topics, and An Introduction to Statistical Learning: With Applications in R by James Gareth et al. covers a range of topics and utilizes statistical software.

Best Beginner Statistics Textbooks: Technical Courses

Professors teaching statistics courses in a mathematical heavy discipline should use technical introductory textbooks. Statistical Inference by George Casella and Roger Berger assumes a basic level of statistical knowledge and focuses on inference in hypothesis testing. Introduction to Mathematical Statistics (Whata€™s New in Statistics) by Robert Hogg, Joseph McKean, and Craig Allen explains probabilities, distributions, parametric and nonparametric tests, and introduction to Bayesian models and is used at Washington University, Yale and Boston University. Reducing planning time for instructors, Mathematical Statistics and Data Analysis by John Rice is a technical introductory text with datasets used at USC, Stanford, Northwestern and the University of Chicago.

Best Beginner Statistics Textbooks: Probability

Instructors at Harvard and Yale selecting textbooks for an introductory course in probability for applied statistics students use Introduction to Probability, 2nd edition, by Joseph Blitzstein and Jessica Hwang for applications in multiple fields. Similarly, A First Course in Probability by Sheldon Ross requires no advanced mathematical background and even starts with a historical understanding of counting to introduce probabilities and is used at Brandeis, NYU, USC and Stanford. Students with backgrounds in calculus find Introduction to Probability, 2nd edition, by Dimitri Bertsekas and John Tsitsiklis, and Probability by Jim Pittman both explain probability theory in technical and illustrative ways. Bertsekas and Tsitsiklisa€™ textbook also contains chapters on introductory statistics.

Best Social Science Statistics Textbooks

The best social science statistics textbooks should account for the often-limited mathematical background of the students and apply research problems students may encounter. Statistical Methods for the Social Sciences by Alan Agresti focuses on models for hypothesis testing commonly found in the discipline and is used at Brandeis, NYU, USC, and Yale. Introduction to Statistics and Data Analysis Using Stata by Lisa Daniels presents an applied approach by combining concepts and software to make doing statistics easier for those without a background in math. The examples and technical language in Applied Statistics for Public and Nonprofit Management by Kenneth Meier et al., would best accompany a statistics course in a public policy program. For instructors looking for a special topics course or a graduate statistics course, Statistics for the Social Sciences: A General Linear Model Approach by Russell Warne has in-depth coverage of the GLM family of models and is used at Rice University and Northwestern.

Best Business Statistics and Econometrics Textbooks

Students in many business and economics courses regularly engage with the building blocks of statistics: algebra and calculus. Additionally, the research questions that business students confront are different from questions in other disciplines. One of the most popular books to the background of the typical student in this discipline used at NYU and USC is Statistics for Business by Robert Stone and Dean Foster. Written by professors from the renowned Wharton Business School, this book includes real-world business problems and data. Statistics for Business and Economics by James McClave et al., has an instructora€™s edition with activity suggestions and answers to the exercises in the student edition. Statistics for Business and Economics by Paul Newbold is an introduction to econometrics with an extensive mathematical component and applications for students across concentrations. Statistics for Business: Decision Making and Analysis by Robert Stine and Dean Foster serves as a guide to understanding how to choose particular models for analyses and is best alongside a technical book and is used at NYU, USC, University of Pennsylvania, and the University of Chicago. Another advanced textbook, Introduction to Statistical Quality Control by Douglas Montgomery, takes principles common in engineering and alters them to fit the emerging business demand for analyses in quality control.

Best STEM Statistics Textbooks

Instructors and students in fields relating to science, technology, engineering, and math (STEM) have strong backgrounds in mathematics. Textbooks for classes in these fields are more in-depth while providing information on applying statistics. Both Probability and Stochastic Processes: A Friendly Introduction for Electric and Computer Engineers by David Goodman and Roy Yates et al., and Probability, Statistics, and Random Processes for Electrical Engineering by Alberto Leon-Garcia focus on probability and random variables commonly used by engineering students. Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering by Alfredo Ang and Wilson Tangfocuses on the same themes of probability and random variables but with simulation models and Bayesian models. As one of the first to apply theories of thermodynamics to specific statical modeling in physics, Statistical Mechanics by Donald Allan McQuarrie (RPI and MIT) is a classical text. Introduction to Modern Statistical Mechanics by David Chandler (used at University of Pennsylvania and MIT) includes modeling in thermodynamics and concepts related to statistical mechanics, such as sampling and simulations. Statistical Physics of Particles by Mehran Kardar (used at Brandeis, USC, and MIT) introduces general statistics and modeling concepts in particle physics.

Best General Advanced Statistics Textbooks

Popular books for instructors of advanced courses provide examples while building on previous knowledge of foundational statistics. Instructors teaching regression analysis can use A Modern Approach to Regression with R by Simon Sheather to focus on regression assumptions and building valid linear and logistic models. Using R as the statistical software for exercises, students create plots and graphs to evaluate models. Classes focusing on applied regression or needing a secondary text alongside a heavily technical textbook can use Regression Analysis by Example by Chatterjee (used at Northwestern and the University of Chicago). Classes on time series analysis can also benefit from interactive texts. Time Series Analysis and its Applications: With R Examples by Robert Shumway and David Stofferteaches the technical aspects and software components of time series models and is used at Stanford and MIT. Time Series Analysis by James Douglas Hamilton is an in-depth text on the subject that does not assume a prior background in R. The right statistics textbook complements the class, the instructor, and the students. The variety of textbooks available offer a range of options but spending time thinking about the needs of the entire class can help instructors find the right statistics textbook.