Contents

Other Sites

Books

1. Introduction
2. Standard error of the estimate
3. Partitioning the sums of squares
4. Confidence intervals and significance tests for correlation and regression
5. Multiple regression
   1. Introduction
   2. Significance tests
   3. Shrinkage
   4. Measuring a variable's importance
6. Regression toward the mean
7. 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