Day 1

	Statistical modelling strategies in  R and S/PLUS
	Linear models: regression and analysis of variance,
	Robust models.
	Generalized linear modelling
	Methods for confidence intervals. Bayesian bootstrap.
	Non-linear and smooth regression
	Additive and generalized additive models
	Hands-on Examples.

Day 2

	Linear mixed effects models.
	Generalized linear mixed effects models
	Non-linear mixed effects models
	Generalized estimating equations (GEE) methods .
	Tree-based models for regression and classification. Implementation with tree and rpart fitting functions.
	Bootstrap aggregation and prediction.
	Classification: linear and quadratic discriminant analysis, Projection pursuit regression. Neural netorkds for classification with extended examples.

Duration: 2 days

Before attending this course, you should have:

	a good general knowledge of statistical estimation and inference methods and a good command of ordinary linear regression.
	Those who want to run the laboratory exercises themselves or who want to use R/S-Plus to use the methods taught in this course in their everyday work should have had a previous introduction to R/S-Plus.

Interested in our training? Please email the

Training Department
XLSolutions Corporation
sue@xlsolutions-corp.com