Text: Ramsey, F. and Schafer, D. (2002). The Statistical Sleuth,
2nd ed. Belmont, CA: Duxbury Press. (The numbers in parentheses
refer to this text.)
Sessions: Two sessions each morning and two each afternoon (except
the last day) yield a total of 18 sessions of about 1.5 hours each. These
are numbered consecutively in the following outline.
1. Overview, pre-program evaluation, and opening activity
2. Simple Linear Regression ’ the basics (7.2, 7.3)
Least squares estimation
Residuals
Sampling distributions of the estimators
Introduction to Data Desk
3. Inference for Simple Linear Regression (7.4)
Inference for slope and intercept
Estimation of a mean response
Prediction of a future value
4. Model Assessment (8.2-8.6)
Graphical tools
Transformations
Analysis of variance for regression
Lack-of-fit
R-squared
Normal probability plots
5, 6. Multiple Regression (9.1-9.6)
Multiple explanatory variables
Constructed explanatory variables ’ curvature, categories, and interaction
Scatterplot matrix
7, 8. Inference for Multiple Regression (10.2-10.4)
Inference for single coefficients
Inference for linear combinations of coefficients
Estimating a mean response
Predicting a future response
Hierarchical models-testing groups of coefficients
9,10. Basic theory of regression using matrices
11,12. Model Checking (11.2-11.6)
Influence and leverage
Partial residual plots
Weighted regression
13. Variable Selection Methods-a brief overview (12.2-12.7)
Multicolinearity
Automated variable selection techniques
14,15. Models for Two-Way Classifications (13.2-13.5)
Additive and nonadditive models
Randomized block v. completely randomized design
Orthogonal contrasts
Multiple comparisons
16. Adjustment for Serial Correlation-a brief overview (15.2-15.5)
17. Logistic Regression for Binary Responses (20.2-20.5)
The logit transformation
Maximum likelihood estimation
Inference for coefficients-deviance
Projects and lab activities will be mixed into these sessions throughout
the week.
Technology: Standard regression software (as listed above) will
be demonstrated, as will some free software that can be downloaded from the
web. Illustrative statistics applets and data sets from various sources
on the web will be introduced, including those referenced on the ASA’s electronic
Journal of Statistics Education.