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From: David Remer <dremer@statsci.com>
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Subject: Advanced SPLUS Training
To: dwmerrill@lbl.gov
Date: Fri, 16 Jan 1998 20:16:44 -0800 (PST)
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Subject: 	Advanced SPLUS Training Course 

MathSoft will be holding an SPLUS Statistical Models class in San Francisco, CA the week of February 18-20. This is an intermediate/advanced level course for SPLUS users that want to learn more about creating models and interpreting results. The tuition for 3 day class is $1050. The course is already partially filled, so please let me know ASAP if you want to attend. A syllabus follows.

David Remer
MathSoft Inc.
800-569-0123 x247

Statistical Models in S-PLUS

Days: 3
CEU: 2.2 (22 contact hours)
Cost: $1050

Course covers the modern modeling methods available with the latest version of S-PLUS. The underlying theory of the modern modeling methods is stressed with hands-on applications using S-PLUS and interpretation of results. The course includes: linear models, ANOVA (including Design of Experiments), generalized linear models, generalized additive models, local regression (LOESS-local smoothers), tree based modeling, and non-linear least squares modeling.

Day 1, AM
Introductions
Overview
	- Overview of statistical models in S-PLUS
	- Review of object types and classes
	- Object orientation
	- Specifying models with formulas
Linear Models
	- Background
	- The Fitting Algorithm
	- Fitting models
	- Extracting information
	- Options in fitting
	- Modifying models
	- Plotting the fits
	- Prediction
	- Repeated fitting
	- Diagnostics

Day 1, PM
ANOVA and Design of Experiments
	- Background
	- Generating experimental designs
	- The Fitting Algorithm
	- Formulas: factors, interactions, nesting, 
	   contrasts
	- Fitting models
	- Plotting the fits
	- Aliasing

Day 2, AM
Generalized Linear Models
	- Background: link and variance
	- The Fitting Algorithm
	- Fitting models
	- Residuals
	- Family generator functions
	- Analysis of deviance
	- Plotting the fits
	- Diagnostics
	- Prediction
	- Stepwise selection

Day 2, PM
Generalized Additive Models
	- Background
	- Spline and local regressions smoothers
	- The Fitting Algorithm
	- Fitting models
	- Plotting the fits
	- Prediction

Day 3, AM
Local Regression (Loess) Models
	- Background
	- The Fitting Algorithm
	- Fitting models
	- Plotting the fits
	- Prediction
	
Day 3, PM
Tree-Based Models
	- Background
	- The Fitting Algorithm
	- Fitting models
	- Plotting the fits
	- Interacting with trees
Nonlinear Models
	- Background
	- The Fitting Algorithms
	- Specifying models
	- Fitting models
	- Plotting the fits