An applied set of modules with focus on the most widely used multivariate methods and their applications in sensory evaluation. Learn about the principle of the methods, the data needed, and the information they provide.
Several clustering methods. Learn about their principle, conditions of use, data preparation phases, common pitfalls as well as good practices. Several real life applications are presented.
Classical linear regression is inappropriate when predictors are correlated – multicollinearity. Learn about Principal Component Regression (PCR) & Partial Least Squares (PLS) regression to deal with multicollinearity and when it is relevant to use them.
Discover a powerful multivariate technique for mapping the consensus among assessors rating series of products/concepts, and for quantifying and mapping the redundancy in sensory descriptors.
Learn how to take data (consumers, genes, …) and organise them into homogeneous groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks. Learn about this very active field of research in statistics and data mining, and discover new techniques.
Research question often require the use of a combination of multivariate data analysis techniques. This course covers advanced multivariate analysis applications for mapping purposes, the selection of representative items in groups, segmentation and prediction, and many more.
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. Discriminant analysis may also be used to build predictive analytics models.
Conceptually similar to PCA, correspondence analysis a method is designed for discovering associations in categorical rather than continuous data. Discover informative 2D-plots for efficient data mapping.
One of the oldest multivariate techniques, factor analysis is closely related to PCA and even confused by many for PCA. However, it serves a totally different purpose. Uncover hidden dimensions in your data.
Learn about Principal Component Analysis, a data reduction technique, to identify, quantify & visualise the structure of a set of measurements. PCA provides insightful data visualisation tools. Learn about innovative applications.