Course

Design of experiments - incompany training

DoE: Practical methods to discover and interpret relationships between factors (with R)

Experiments play an important role in understanding and controlling systems and processes. With the aid of methods for statistical design of experiments, it is immediately possible to set up these experiments efficiently and to analyze and interpret their results.

It is important to formulate a clear problem description, identify important influencing factors and choose the right experimental techniques. It is equally important to determine how many experimental runs are needed and which factor combinations should be measured.

Principles of experimental design are successfully applied in a wide variety of areas, but especially in industry, where experiments becoming increasingly important to analyze and to improve processes.

Statistical techniques for experimental design and data analysis

This DoE-course:

  • Provides knowledge and skills in the application of statistical techniques for experimental designs.
  • Gives you experience in analyzing the data obtained.
  • Teaches you to apply the methods discussed independently in your own work environment.
  • Teaches you to use relevant statistical software such as R in a responsible way.

Intended for

Process, product or quality engineers and other technicians and scientists involved in the development and optimization of processes and products. The course is also suitable for lecturers from universities and colleges of higher education who want to learn more about design of experiments and data analysis.

Knowledge of basic statistical techniques such as testing, estimation and regression modelling is desirable, as is some experience in the use of (elementary) linear algebra and statistical software.

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

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  • Program

    During the course, practical examples and assignments are discussed. Participants gain experience with the use of the statistical software R for Design of Experiments.

    Topics treated:

    • Introduction to Design of experiments.
    • Review: a birds' eye review of engineering statistics.
    • Sample size and its influence on modeling accuracy.
    • Balanced and unbalanced designs, cofactors,  blocking and randomization.
    • Screening designs: factorial, blocked and fractional designs.
    • Response surface methods and designs.
    • Optimization on the basis of response surface models.
    • Mixture designs, robust designs, D-optimal designs.
    • Epilogue: review, trends and next steps.