Day 1

	Statistical modelling in R: Modelling strategies, purposes, R tool and operating paradigms, trellis graphics for data presentation and inspection.
	Classical linear models: regression and analysis of variance, model fitting - choice of variables, use of the AIC and competitor criteria for model selection, stepwise methods and their hazards -. Diagnostics and transformations.
	Robust and resistant methods.
	Generalized linear modelling, Logistics regression, Log-linear models, Negative binomial and Multinomial models.
	Classical and bootstrap methods for confidence intervals. Bayesian bootstrap.
	Non-linear and smooth regression: Least squares non-linear regression, model, fitting and diagnostics. Alternative algorithms.
	Penalized likelihood methods: Additive and generalized additive models: fitting, display and prediction. ACE and AVAS exploratory techniques.
	Hands-on Examples.

Day 2

	Linear mixed effects models. Model fitting and diagnostic inspection. Estimation and prediction.
	Generalized linear mixed effects models: fitting procedures and diagnostic checking.
	Non-linear mixed effects models. Fitting procedures and key examples.
	Generalized estimating equations (GEE) methods .
	Tree-based models for regression and classification. Implementation with tree and rpart fitting functions. Pruning and model selection by cross-validation.
	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