312 references

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 Orbis 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.

R.P. Abelson (1995) Statistics as Principled Argument. Erlbaum Associates.
- 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. Springer.
- 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
- A classic. For related materials poke around in the R. A. Fisher Digital Archive. For example, look at 204.
D. Freedman (2005) Statistical Models: Theory and Practice
- Insightful, modern text.
D. Freedman (2010) Statistical Models 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.) Matrix computations
- 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.)
- Viewable online.
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) Multivariate Analysis
- Good source for mathematics of principal components, etc.
F. Mosteller and J.W. Tukey (1977) Data Analysis and Regression: a second course in statistics. Wiley.
- Much wisdom about regression.
H. Scheffé (1959) The Analysis of Variance. Wiley.
- 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.

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