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Learn about preference mapping techniques to explore and understand consumer preferences. Applications dealing with segmentation and the identification of niche markets are discussed. Focus on pitfalls and good practices.

The primary goal of this method is to discover which variables have the best ability of discriminating between two or more known groups in your data. Discrimimant analysis may also be used to build predictive analytics models.

Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good are no simple tasks. Learn the essentials of model-building, goodness-of-fit tools and common pitfalls.

This module offers an overview of tree-based modeling techniques. Learn how they work, when to use them, their strengths and weaknesses, and their implementation including validation. Several applications are presented.

Classical linear regression is inappropriate when the predictors are correlated (multicollinearity). Learn the principe of PCR and PLS regression designed to deal with multicollinearity and when it is relevant to use them.

Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead. Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.