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Modeling Techniques In this category
Relating several variables is an essential goal in statistics. This goal may be achieved using a variety of techniques. The choice of the technique depends on the exact nature of the problem and the variables considered. Whether you work in research and development, in finance or in marketing, our training sessions offered in this section can help you. We cover not only multiple linear regression and logistic regression, but also more recent techniques such as Partial Least Squares (PLS) and Classification and Regression Trees (C&RT).
In each of the sessions outlined below, we discuss the strengths and the weaknesses of each technique as well as the technique’s practical implementation and the interpretation of results produced by the use of the technique.
    Session Name+   Summary   Length (days) 
 Simple and Multiple Linear Regression Techniques  Simple and Multiple Linear Regression Techniques  This workshop deals with simple and multiple linear regression techniques.
Fundamental principles used in linear regression modeling are first introduced. Then focus is put on the conditions of use, tools to assess model performance (mostly plots), and the difference between explanatory and predictive models is explained. Participants will learn which common pitfalls to avoid and the correct interpretation of tables and graphical output produced by statistical software. Advanced methods, such as variable selection methods ("stepwise,"best subset", etc.), the use of categorical explanatory variables, the inclusion of nonlinear and interaction terms (polynomial regression) and ways to deal with the problem associated with correlated explanatory variables (multicolinearity) are also covered. 		  1.0 
 Regression Models for Categorical Data  Regression Models for Categorical Data  This workshop deals with regression models for categorical response variables. A strong emphasis is put on logistic regression.
Fundamental principles underlying binary data modeling is first explored through the inadequacy of linear regression. Then the alternative model logit or logistic regression is presented. Similarities with linear regression are pointed out and tools specific to logistic regression are covered in detail:
  • odds ratio used to quantify the magnitude of effects
  • specificity, sensitivity, ROC curves
  • cross-validation techniques.
    Generalization of the logistic regression model to the case in which the response variable has more than levels is illustrated. 
    		•   1.0 
     Regression on Principal Components and PLS Regression  Regression on Principal Components and PLS Regression  This workshop covers the problem of correlated explanatory variables (multicollinearity) in regression models, its impact on the model performance and discusses PLS regression, the most popular technique to deal with this problem.
  • It first introduces the underlying concepts in PLS regression through its link with ultivariate methods, namely Principal Component Analysis (PCA). Focus is also put on strategies used to measure the predictive ability of models and their usefulness to optimise the PLS model-building phase.
  • Case studies are used so that participants gain confidence in the interpretation of software output and to make them aware of the conditions of use and common pitfalls. 
    		•   1.0 
     Classification and Regression Trees  Classification and Regression Trees  This half-day workshop offers an overview of tree-based modeling techniques, starting with the interpretation of results, strengths and weaknesses and also discusses their practical realization.   0.5 
     Life, Reliability and Survival Models  Life, Reliability and Survival Models  This workshop deals with modeling techniques used for life data (time-to-event) and their specific features, the interpretation of results, strengths and weaknesses and also discusses their practical realization. Several applications in the social sciences, engineering and life sciences will be presented.   1.0 
     Workshop on Regression Techniques  Workshop on Regression Techniques  This workshop gives participants the opportunity to put into practice the methods seen during the different seminars on regression analysis and to apply them on their own data sets using their own statistical software with the support of experienced instructors here to assist them.   0.5 
     Nonlinear Regression  Nonlinear Regression  This workshop offers an overview of nonlinear regression. This special type of regression technique is commonly used to model growth curves and to establish relationships between dose and responses. We discuss the strengths and weaknesses of the techniques, their practical implementation and the interpretation of results produced from the use of these techniques.   0.5 
     Displaying 1 to 7 (of 7 sessions)   Result Pages:  1  
    		 
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