Course: Multivariate Data Analysis

Detect patterns and relationships in multiple variable datasets (with R)

This course provides knowledge and skills in the application of common statistical techniques used, for example in an industrial context, for the analysis of multivariate data related to quality aspects of products and processes.

The techniques discussed are ideal for discovering relationships between groups of variables, for classifying measurement results or for detecting patterns and clusters in experimental data. Usually, several factors or variables influence the result.

Based on multivariate data analyses, for example, parameter settings in a production process can be related to quality characteristics of the resulting product. They also make it possible to detect combinations of parameter setting where a process becomes unstable or to calibrate a measurement procedure.

Apply multivariate statistical methods

In this course you will learn to:

• Analyze and model variables that are correlated.
• Identify variables that have a significant influence on a process.
• Provide options for improved process control.
• Detect causes of production problems.
• Apply multivariate methods as discussed independently in your own work situation.
• Use representative statistical software for multivariate data analysis, such as R, R Studio, Minitab, JMP or IBM-SPSS.

Intended for

Academics, technicians and professionals working in the field of chemometrics, sensory data analysis or quality control of products and processes. The course is also suited for lecturers at universities or colleges of higher education who want to be informed on actual methods for multivariate data analysis.

Knowledge of basic statistical techniques like testing, estimating and regression modelling is assumed. Some experience in the use of (elementary) linear algebra and statistical software is desirable.

In consultation with the participants this course can be taught in Dutch or English.