Stat 312/612 fall 2016
Texts and references
revised 25 Aug 2016
There is no single text for this course. I have drawn material from the original literature and from the following sources.
If you are accessing the web through a Yale machine (or if you have taken steps to enable
off campus access)
then you should already have access to some of these documents. It is a good idea to
search for a title
to see if Yale has negotiated some form of online access.
When I know a legal way to find something I have added a link.
For illegal methods you need to consult with a Yale student.
Philosophy of statistical inference
Linear algebra and computing
R.P. Abelson (1995) Statistics as Principled Argument.
- Highly readable discussion of how to make a statistical argument.
Very little Math and not much on linear models.
Sheldon Axler (2015) Linear Algebra Done Right.
- My favorite book for understanding linear algebra. Beautifully written. Look at Chapter 5, on eigenvalues, for an elegant modern treatment.
- A. C. Atkinson Plots, Transformations and Regression
Nice discussion of diagnostic plots.
- D. A. Belsley, E. Kuh, and R. E. Welsch (1980)
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley.
Discussion of SVD as a tool for sensitivity analysis.
- Box, Hunter and Hunter (1978) Statistics for Experimenters: An
Introduction to Design, Data Analysis, and Model Building
Excellent reference. Not too heavy on the mathematics. Clear.
J. M. Chambers (1977) Computational Methods for Data Analysis. Wiley.
- Good explanation of why X(X'X)-1X' is not the way to go.
- J. M.Chambers, W. S. Cleveland, B. Kleiner, and P. A. Tukey (1983)
Graphical Method for Data Analysis
Very clear explanations about standard diagnostic plots.
- J.M. Chambers and T.J. Hastie (1992)
Statistical Models in S
Detailed descriptions of model fitting in S.
R.D. Cook and S. Weisberg Residuals and Influence in Regression
R. A. Fisher (1935 first ed., eight editions) The Design of Experiments
D. Freedman (2005)
Statistical Models: Theory and Practice
D. Freedman (2010)
and Causal Inference
Provocative collection of articles by a leading critic of the way statistical practice has evolved. The book might change your mind about the role of statistics.
Golub and Van Loan (1983 first ed.; 1989 second ed.; 2013 fourth ed.)
Good discussion of SVD and least squares. The book has gotten bigger and more encyclopedic with each edition.
W. Kruskal (1961)
The coordinate-free approach to Gauss-Markov estimation, and its application to missing and extra observations.
Fourth Berkeley Symposium, Volume 1
Important historical paper.
- LINPACK Users' Guide (1979--1993).
General descriptions of algorithms. Clear. (Splus uses some
LINPACK ideas. MatLab grew out of LINPACK, I think.)
- P. McCullagh and J. A. Nelder (1983 first ed., 1989 second ed.)
Generalized Linear Models
The standard reference. Big second edition.
- K. V. Mardia, J. T. Kent, and J. M. Bibby (1979)
Good source for mathematics of principal components, etc.
- F. Mosteller and J.W. Tukey (1977)
Data Analysis and Regression: a second course in statistics.
Much wisdom about regression.
- H. Scheffé (1959)
The Analysis of Variance.
Thorough for its time. Not for bedtime reading.
- R documentation
W. N. Venables and B. D. Ripley (2002)
Modern Applied Statistics with S
A good general reference for the S language and its offspring (including R). It also has very terse accounts of several topics in this course.