A Reference List for Statistics
The following books are useful for learning/reference. Some books overlap content and have different levels of difficulty.
Mathematical Statistics
- Introduction to Mathematical Statistics by Robert Hogg, et al.
- In All Likelihood by Yudi Pawitan
- The Matrix Cookbook by Kaare Petersen and Michael Pedersen
- Matrix Algebra: Theory, Computations, and Applications in Statistics by James Gentle
- A First Look at Rigorous Probability Theory by Jeffrey Rosenthal
Bayesian Statistics
- Doing Bayesian Data Analysis by John Kruschke
- Bayesian Data Analysis by Andrew Gelman, et al.
- Statistical Rethinking by Richard McElreath
- Regression and Other Stories by Andrew Gelman, et al.
Modeling
- How to create a model
- Regression Modeling Strategies by Frank Harrell
- Data Analysis Using Regression and Multilevel Models by Andrew Gelman and Jennifer Hill
- The Book of Why by Judea Pearl and Dana Mackenzie
- Causal Inference by Miguel A. Hernan and James M. Robins
- Statistical Issues in Drug Development by Stephen Senn
- Clinical Prediction Models by Ewout Steyerberg
- Uncertainty by William Briggs
- Regression Analysis: A Constructive Critique by Richard Berk
- Implementing specific models
- General
- Applied Linear Statistical Models by Michael Kutner, et al.
- Categorical Data Analysis by Alan Agresti
- Linear Mixed Models: A Practical Guide Using Statistical Software by Brady West, et al.
- Extending the Linear Model with R by Julian Faraway
- Computer Age Statistical Inference by Bradley Efron and Trevor Hastie
- Multivariate
- Methods of Multivariate Analysis by Alvin Rencher and William Christensen
- An Introduction to Applied Multivariate Analysis with R by Brian Everitt and Torsten Hothorn
- Multivariate Data Analysis by Joseph Hair, et al.
- Statistical Learning
- An Introduction to Statistical Learning by Trevor Hastie, et al.
- Applied Predictive Modeling by Max Kuhn and Kjell Johnson
- Survival Analysis
- Survival Analysis: A Self-Learning Text by David Kleinbaum and Mitchel Klein
- Modeling Survival Data: Extending the Cox Model by Terry Therneau and Patricia Grambsch
- Time Series
- Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos
- Quantile Regression
- Handbook of Quantile Regression by Roger Koenker, et al.
- Missing Data
- Flexible Imputation of Missing Data by Stef van Buuren
- General
Design of Experiments
- Statistics for Experimenters: Design, Innovation, and Discovery by George Box, et al.
- Design and Analysis of Experiments by Douglas Montgomery
- The Design of Experiments: Statistical Principles for Practical Applications by Roger Mead
- Design and Analysis of Experiments with R by John Lawson
Programming
- R
- Advanced R by Hadley Wickham
- The Art of R Programming by Norman Matloff
- R for Data Science by Hadley Wickham and Garrett Grolemund
- Software for Data Analysis by John Chambers
- Extending R by John Chambers
- R Packages by Hadley Wickham
- Reproducible Documents
- R Markdown by Yihui Xie, et al.
- Dynamic Documents with R and knitr by Yihui Xie
- bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie
- Reproducible Research with R and RStudio by Christopher Gandrud
- LaTeX and Friends by Marc van Dongen
- More Math Into LaTeX by George Gratzer
- Python
- Python for Data Analysis by Wes McKinney
- Python Data Science Handbook by Jake VanderPlas
- Think Python by Allen Downey
- Automate the Boring Stuff with Python by Al Sweigart
- SQL and Databases
- The Language of SQL by Larry Rockoff
- Data Analysis Using SQL and Excel by Gordon Linoff
- Data Modeling Essentials by Graeme Simsion and Graham Witt
- C++
- C++ Primer by Stanley Lippman, et al.
- Effective Modern C++ by Scott Meyers
- Seamless R and C++ Integration with Rcpp by Dirk Eddelbuettel
- C
- C Programming Absolute Beginner’s Guide by Greg Perry and Dean Miller
- C Programming: A Modern Approach by K. N. King
- Modeling with Data: Tools and Techniques for Scientific Computing by Ben Klemens
Data Visualization
- Visualizing Data by William Cleveland
- ggplot2 by Hadley Wickham
- The Grammar of Graphics by Leland Wilkinson, et al.
- Exploratory Data Analysis by John Tukey
- Data Visualization by Kieran Healy
- Fundamentals of Data Visualization by Claus O. Wilke
Sampling and Surveys
- Sampling by Steven Thompson
- Survey Sampling by Leslie Kish
- Applied Survey Data Analysis by Heeringa, et al.
- The Survey Research Handbook by Pamela Alreck and Robert Settle
Measurement
- An Introduction to Error Analysis by John Taylor
Mathematical Background
- Linear Algebra by David Poole
- Calculus by Morris Kline
- Book of Proof by Richard Hammack
Published: 2015-10-01
Last Updated: 2021-12-27
Last Updated: 2021-12-27