Texts and references

• 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.
• 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
  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
  Good explanation of why X(X'X)^-1X' is not the way to go. Excellent discussion of least squares via QR.
• 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.
• Golub and Van Loan (1983 first ed., 1989 second ed.) Matrix computations
  Good discussion of SVD and least squares
• LINPACK Users' Guide (1986).
  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) 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
  Much wisdom about regression.
• C. R. Rao (1973) Linear Statistical Inference and Its Applications
  Encyclopaedic.
• C. R. Rao and H. Toutenburg (1995) Linear Models: Least Sqaures and Alternatives
  I have only glanced at this book. It appears to cover many of the topics from 312/612. Worth a second look.
• H. Scheffé (1959) The Analysis of Variance
  Thorough for its time. Not for bedtime reading.
• S-plus documentation
  Follow the links from http://www.insightful.com/products/desktop.asp
• W. N. Venables and B. D. Ripley (1997?) Modern Applied Statistics with S-Plus
  You might find it useful for Splus. It also has very terse accounts of several topics in this course.
• G. B. Wetherill (1986) Regression Analysis with Applications
  Concise discussion of several topics covered by the course (such as multicollinearity).

Papers

• J. W. Longley "An appraisal of least squares programs for the electronic computer from the point of view of the user", JASA 62 (1967) 819-841.
• A. E. Beaton, D. B. Rubin, J. L. Barone "The acceptability of regression solutions: another look at computational accuracy", JASA 71 (1976) 158-168. (See also the followup note by Dent and Cavendar JASA 72 (1977) 598-602, which includes more comments from BRB.)