Multivariate data analysis

In-company
In English on request

Learn how to use the most common techniques used in an industrial context for the analysis of multivariate data.

Detect patterns and relationships in multiple variable datasets

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

During this course you will learn:

  • 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.
  • Possibilities and dangers of using generative AI, such as ChatGPT and Copilot, when performing a multivariate data analysis in practice.

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. Practical examples are in R.

Would you like more background information? Read the interview:
Course leader dr. Koo Rijpkema (University of Technology Eindhoven) shares his vision on the world of data and courses and the importance of the discipline.

In English on request

Do you want to follow the course in English? Please mention this in the remarks field when you register.

Course leader

Data analysis and programming

dr. Koo Rijpkema

Eindhoven University of Technology (TU/e)

“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”

This course is rated with an average of

9,3

Program manager

Why PAOTM

  • The latest post-academic knowledge and skills
  • Focused on questions that arise in a technical environment
  • Interactive and directly applicable in practice
  • Top teachers from science, research and business

Frequently asked questions

  • "Goede inleiding in de toepassing van multivariate data analyse, inclusief uitstekende opfrisser van de meer basale statistiek."
    Frank van Boven
    Erasmus MC
  • "Uitstekende cursus, veel aan bod geweest, hoog niveau. Zeer goede, enthousiaste docent."
    Participant
    Nouryon Industrial Chemicals BV
  • "Interessante diepgaande cursus op hoog niveau, gegeven door een kundige enhousiaste cursusleider."
    Participant
    Sitech Services BV
  • "Een goede cursus die breed de multivariate analyse toelicht."
    Participant
    Het Waterlaboratorium
  • "Leerzaam, adaptief, goed voorbereid."
    Joachim Verhagen
    ASML Netherlands BV

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In addition to the course offerings, the Study Guide also contains the themes that we will further develop next year. Would you like a complete overview of our courses and training in your field(s)? Request the Study Guide and receive it digitally.