# 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

## Bayesian Ideas

- Doing Bayesian Data Analysis by John Kruschke
- Bayesian Data Analysis by Andrew Gelman, et al.
- Statistical Rethinking by Richard McElreath

## Modeling

- 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
- An Introduction to Statistical Learning by Trevor Hastie, et al.
- Applied Predictive Modeling by Max Kuhn and Kjell Johnson

- 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.

- 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

## 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
- Software for Data Analysis by John Chambers
- R Packages by Hadley Wickham

- Reproducible Documents
- Dynamic Documents with R and knitr 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
- Data Science from Scratch by Joel Grus
- Automate the Boring Stuff with Python by Al Sweigart

- SQL
- The Language of SQL by Larry Rockoff
- Data Analysis Using SQL and Excel by Gordon Linoff

- C++
- Programming: Principles and Practice Using C++ by Bjarne Stroustrup
- 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

## 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

## Mathematical Background

- Linear Algebra by David Poole
- Calculus by Morris Kline
- Book of Proof by Richard Hammack

## Other

- Ethics
- On Being a Scientist: A Guide to Responsible Conduct in Research by The National Academies

- Communication
- 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

- Statistical
- 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