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Market Research & Business In this category
This category contains applied statistics training sessions intended for people working in market research and business, seeking to harness the power of statistical tools to detect tendencies and form segments or groups of interest from the data collected.
    Session Name+   Summary   Length (days) 
 Marketing and Segmentation  Marketing and Segmentation  This course is a market research and business-oriented version of our popular cluster analysis session. It covers the most commonly used segmentation techniques, from the classical hierarchical methods that can be used when the dataset size is not too large to techniques aimed at dealing with large files like the k-means method. More recent methods are also discussed. A special emphasis is put on the issues related to combining segmentation methods along with other statistical tools in order to get the most relevant information out of a consumer database.   2.0 
 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 
     Preference Mapping in Practice  Preference Mapping in Practice  This applied training session in statistics is intended for all the people collecting preference data and wishing to determine consumer segments of product preferences in order to identify market opportunities.
    This course covers powerful preference mapping techniques to explore and understand the preference structure of consumers.     		  1.0 
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