A Reference List for Statistics
The following books are useful for learning/reference. Some books overlap content and have different levels of difficulty.
- 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
- 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.
- 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
- 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
- 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
- Survival Analysis
- 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
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
- Reproducible Documents
- SQL and Databases
- 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
- 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
- An Introduction to Error Analysis by John Taylor
Last Updated: 2021-12-27
Last Updated: 2021-12-27