A Reference List for Statistics
The following books are useful for learning/reference. Together they represent what I think a graduate program should focus on. Some books overlap content and have different levels of difficulty.
Mathematical Statistics and General Ideas
- Introduction to Mathematical Statistics by Robert Hogg, et al.
- The Matrix Cookbook by Kaare Petersen and Michael Pedersen
- Matrix Algebra: Theory, Computations, and Applications in Statistics by James Gentle
- In All Likelihood by Yudi Pawitan
- 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
- General Modeling
- Applied Linear Statistical Models by Michael Kutner, et al.
- Data Analysis Using Regression and Multilevel Models by Andrew Gelman and Jennifer Hill
- 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
- Regression Modeling Strategies by Frank Harrell
- Statistical Learning
- 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.
- Survival Analysis
- Time Series
- Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos
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
- 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
Graphing and Exploratory Data Analysis
- Visualizing Data by William Cleveland
- The Elements of Graphing Data by William Cleveland
- ggplot2 by Hadley Wickham
- The Grammar of Graphics by Leland Wilkinson, et al.
Sampling and Surveys
- Sampling by Steven Thompson
- Survey Sampling by Leslie Kish
- The Survey Research Handbook by Pamela Alreck and Robert Settle
- On Being a Scientist: A Guide to Responsible Conduct in Research by The National Academies
- The Craft of Scientific Writing by Michael Alley
- Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded by Joshua Schimel
- The Craft of Scientific Presentations by Michael Alley
- Ecological Methodology by Charles Krebs
- Mixed Effects Models and Extensions in Ecology with R by Alain Zuur, et al.
- Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving by Deborah Nolan and Duncan Temple Lang
- Optimal Design of Experiments: A Case Study Approach by Peter Goos and Bradley Jones
- Practical Data Science with R by Nina Zumel and John Mount