In an industrial environment, it’s always a challenge to meet the quality and robustness requirements of your customer. For engineers it’s therefore crucial to understand the process and critical parameters influencing product performance. And that requires being able to analyze and interpret data. In some cases, there’s a lot of data available and the challenge is to summarize it or find data relationships. In other cases, there’s only very limited data available and the challenge is drawing reliable conclusions.

This training course involves the use and application of the most important statistical methods for the (semiconductor) industry. During the training, methods and software tools from real industrial practice are applied.



As with all CQM training, this program is only given as in-company training tailored to the daily practices of the participants and their company. Here’s an example of the content of a training course in the semi-conductor industry:

  • A summary and presentation of the data in graphs and statistics;
  • The relationship between parameters and regression analysis;
  • Sampling: the interpretation of characteristics within a population;
  • Distribution of statistical characteristics and its use;
  • Estimating population parameters based on samples and their characteristics;
  • Significance testing (hypothesis tests such as F and t-test), confidence intervals, sample size and its impact on the reliability of the conclusions;
  • Interpretation and assessment of processes, process performance, Ppk and an introduction to process control (SPC);
  • Interpretation of variance, variance components, variance analysis;
  • Assessment and improvement of Gage R&R study measurement processes, Measurement System Assessment, repeatability and reproducibility, accuracy, and Measurement System Comparison (MSC).


By the end of the training

  • Engineers and technicians can summarize and interpret using statistical methods, and draw and present reliable conclusions.

Applications include:

  • Interpretation of sample data and confidence intervals;
  • Significance testing, sampling techniques and sampling;
  • Statistical analysis of processes, assessment of process performance/capability, and an introduction to process control (SPC);
  • Assessment and improvement of measurement processes (Gage R&R study).


Want to know more about Industrial Data Analysis?

Get in touch with Bert Schriever.