R help - Apr 2007 - Course***New R course by Dr Bill Venables: Traditional and Modern Approaches to Statistical Modelling with R / Washington, DC July 12-13

XLSolutions Corporation is proud to announce our July 12-13, 2007
Traditional and Modern Approaches to Statistical Modelling with R  - in
 Washington, DC by Dr Bill Venables.
http://www.xlsolutions-corp.com/RStatsBV.htm


**** Washington DC, July 12-13, 2007

Reserve your seat now at the early bird rates! Payment due AFTER
the class

Course Description:

R and R+ offer a large choice of facilities for classical and modern
approaches to statistical modelling. Dr Bill Venables will present R as
a complete data analysis and graphics environment and will focus on R
programming strategies for handling standard and non-standard
statistical modelling problems with the following outline:

- 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.  
- 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.  
- Hands-on Examples.  



Email us for group discounts.
Email Sue Turner: sue at xlsolutions-corp.com
Phone: 206-686-1578
Visit us: www.xlsolutions-corp.com/training.htm
Please let us know if you and your colleagues are interested in this
classto take advantage of group discount. Register now to secure your
seat!

Interested in R/Splus Advanced course? Coming up in San Francisco and
Seattle  - July 2007 - email us.


Cheers,
Elvis Miller, PhD
Manager Training.
XLSolutions Corporation
206 686 1578
www.xlsolutions-corp.com
elvis at xlsolutions-corp.com