Texts and references
There is no single text for this course. I have drawn material from the original literature and from the following books.
 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)^{1}X' 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.
 Splus documentation
Follow the links from http://www.insightful.com/products/desktop.asp

W. N. Venables and B. D. Ripley (1997?)
Modern Applied Statistics with SPlus
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
JASA = Journal of the American Statistical Association
 J. W. Longley "An appraisal of least squares programs for the
electronic computer from the point of view of the user", JASA 62 (1967)
819841.

A. E. Beaton, D. B. Rubin, J. L. Barone "The acceptability of
regression solutions: another look at computational accuracy", JASA 71
(1976) 158168. (See also the followup note by Dent and Cavendar JASA
72 (1977) 598602, which includes more comments from BRB.)