Statistical modelling

Operational process understanding is the basis for ensuring quality and maximizing yield. Without operational process understanding, process output often is not predictable nor capable. One method to gain process understanding is through analyzing historical data.

This course will enable you to analyze historical data and detect:

  • Difference between groups i.e. deferent machines, lots, shifts etc.
  • Significance of effects
  • Correlating factors

It will furthermore enable you to build and reduce models in JMP from SAS.

This course consists of many practical calculation examples and describes many common pitfalls when analyzing historical data.


  • Hypothesis testing
  • Finding correlations between causes (x) and effects (y) by performing linear and multiple regression analysis
  • Brainstorming and prioritizing critical process parameters (CPPs) that are will be varied in a DoE
  • Model building and reduction


Basic Statistics and Data Quality course or similar prior knowledge


The software used in this course is JMP from SAS and the instructors will demonstrate all the exercises in JMP. If you are currently not a JMP user, we recommend you install JMP on your computer before participating in the course. A free 30-day trial can be found in JMP homepage. If you need assistance in installing JMP or have any questions do not hesitate to reach out to

If you are using another statistical tool, we can arrange a course using your preferred tool. Contact us to hear more about this possibility.


Venue: NNE's headquarters, Bredevej 2, 2830, Virum, Denmark

Price: DKK 5,000 + VAT (includes course materials, refreshments and lunch)

Language of instruction: The course will be held in English unless all participants speak and understand Danish

Cancellation disclaimer: We reserve the right to cancel the course due to instructor illness or low participant numbers. If this is the case, we will inform you in good time and offer a full refund where applicable.

Duration: 1 day (08:30-16:30)

Course dates:

  • 1 November 2024

Deadline for sign-up:

2 days before the course starts

Sign up for the course