Multivariate data analysis

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

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. Practical exercises are in R.

The techniques demonstrated with R, can also be performed in Python and yield comparable results. Participants receive sample data files so that they can reproduce the results using their preferred data analysis software. It is possible to use R from Python and Python from R and combine the advantages of each. Do you want to use Python or discover more possibilities of Python? View our course Python for engineers.

Also take a look at the overview of all data analytics and statistics courses.

Interested in related courses? Also have a look at the course Practical data scienceData mining & predictive modelingTime series analysis and forecasting, Python voor ingenieurs,  Design of Experiments (InCompany training) en Essentials of predictive analytics (InCompany training).    

Curious about the background of course leader Koo Rijpkema and his vision on the importance of data in the world of technology? Read the interview!

Share this page

  • Information
    The program can be taught in English on request.
  • Program

    Throughout the course practical examples and assignments are discussed. Participants will gain experience in the use of the statistical software R and R Studio.

    Topics treated:

    • Introduction and overview
    • Exploratory Data Analysis (EDA)
    • Multivariate testing and Estimation (MANOVA)
    • Principal Component Analysis (PCA)
    • Factor Analysis (FA)
    • Discriminant Analysis
    • Cluster Analysis
    • Canonical Correlation
    • Principal Component Regression and Partial Least Squares Regression (PCR/PLS)
    • Multidimensional Scaling
  • Reviews
    This course is assessed with a 9.3
    “Useful, adaptive, well-prepared.”
    Joachim Verhagen, ASML Netherlands BV
    “A good course that broadly explains the multivariate analysis.”
    Employee of Het Waterlaboratorium
    “Interesting and profound high level course, taught by a professional enthusiastic course leader.”
    Employees of Sitech Services BV
    “Good introduction in applying multivariate data analysis, including excellent refreshing theory on the basics of statistics.”
    Frans van Boven, Erasmus MC
    “Educational including many examples. I did really appreciate the ability of the professor in including and adapting further examples based on the specific issues the participants are facing and trying to solve with MDA.”
    Tomaso Della Vedova, Endress en Hauser BV
    “Excellent course, many topics have been adressed, high level. Very good, enthousiastic teacher.”
    Employee of Nouryon Industrial Chemicals BV