Data Analysis
in an industrial environment
In an industrial environment, meeting customer requirements for quality and robustness is always a challenge. For engineers, it is therefore essential to understand the processes and critical parameters that affect product performance. This requires the ability to analyze and interpret data. In some cases, large amounts of data are available and the challenge is to summarize the data and identify relationships. In other cases, only limited data is available and the challenge is to draw reliable conclusions. This training covers the use and application of the most important statistical methods for the (semiconductor) industry. The methods and software tools used during the training are applied to real-world industrial challenges.
As with all CQM training courses, this is an in-company training tailored to the participants’ day-to-day work and their organization. An example of the content of a training course for the semiconductor industry is provided below.
Example of content
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Summarizing and presenting data using graphs and statistics
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Relationships between parameters and regression analysis
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Sampling and interpreting population characteristics
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Statistical distributions and their application
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Estimating population parameters based on samples and their characteristics
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Significance testing (hypothesis testing, including F-tests and t-tests), confidence intervals, sample size and their impact on the reliability of conclusions
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Interpreting and assessing processes and process performance, Ppk and an introduction to Statistical Process Control (SPC)
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Interpreting variance, variance components and Analysis of Variance
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Assessing and improving measurement processes: Gage R&R studies, Measurement System Assessment, repeatability and reproducibility, accuracy and Measurement System Comparison (MSC)
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By the end of the training
Engineers and technicians will be able to use statistical methods to summarize and interpret data, draw reliable conclusions and present their findings. Applications include:
- Interpreting sample data and confidence intervals
- Significance testing, sampling techniques and sample selection
- Statistical analysis of processes, assessing process performance and capability, and an introduction to Statistical Process Control (SPC)
- Assessing and improving measurement processes using Gage R&R studies.
Want to know more about Data Analysis for industrial applications?
Contact Bert Schriever.
