 Introduction
 Standard error of the estimate
 Partitioning the sums of squares
 Confidence intervals and significance tests
for correlation and regression
 Multiple regression
 Introduction
 Significance tests
 Shrinkage
 Measuring a variable's importance
 Regression toward the mean
 Exercises


Analysis
Tools
Analysis
Lab
Rice Virtual Lab in Statistics
JavaStat
by John Pezzullo
WebStat
by Webster West
VassarStats
by Richard Lowry
Ordinary
least squares
by College of St. Benedict
Instructional Demos
Components
of r
Rice Virtual Lab in Statistics
Regression
by eye
Rice Virtual Lab in Statistics
Restriction
of range
Rice Virtual Lab in Statistics
Reliability
and regression analysis
Rice Virtual Lab in Statistics
Leverage
and influence in simple regression
by Robert McCulloch
Linear
regression
by Charles Stanton
Put
points
by Stat Dept, U. of Illinois
Regression
applet
by Webster West
Text
Inferences
for regression
by H. J. Newton, J. H. Carroll, N. Wang, and D. Whiting
Regression, Errors
in Regression
by P. B. Stark
Multiple regression
by StatSoft
Regression
by G. David Garson.
Linear
regression, multiple
regression
by Sunkara, V. Patil, R. Bellary, G. Quisumbing, H. Le, and Z. Zhou
The general linear
model, Regression
toward the mean by William Trochim
Correlation
coefficient
by Will Hopkins of the University of Otago
alternatives
to least squares
by T. Kirkman

Multiple
Regression : A Primer (The Pine Forge press Series in Research Methods
and Statistics) by Paul D. Allison.
Multiple
Regression : Testing and Interpreting Interactions
by Leona S. Aiken, Stephen G. West, Raymond R. Reno
Applied
Linear Statistical Models (Irwin Series in Statistics) by Michael
H. Kutner, Christopher J. Nachtschiem, William Wasserman, and John Neter
