This course builds on the DOE 1 course. Now, the statistical level is just a bit higher and you learn to become even sharper to test the factors that matter most to your problem through the DOE (Design of Experiments) method.
This course focuses on, among other things, hypothesis testing on non-normalized data, analysis of data sets with many variables, cross validation and DOE with whole plots and variance modeling. Using a variety of advanced statistical tools, you learn to analyze and extract important information from your data set.
Target Group for DOE 2
This module is intended primarily for people who work with the development and optimization and with a need to implement more advanced statistical analyses that can provide help developing and optimizing your products or processes.
What will you learn?
- Make hypothesis testing on non-normally distributed data
- Fit asymptotic curves
- Make linearity studies with heterogeneous variances and cross-validation
- Analyze data sets with many variables by using PCA and PLS
- Analyze DoE data with all the plots, variation modeling, and design space
Content in bullets
- Hypothesis testing with non-normally distributed data
- Fit of asymptotic curves
- Time series analysis
- Linearity studies with inhomogeneous variances
- Regression analysis with the censored data
- Multicollinearities – Variance Inflation Factor (VIF)
- Principal Component Analysis (PCA)
- Partial Least Square (PLS)
- DoE with plots
- DoE with variation modelling
- DoE with Design Space
In order to attend this course, you must have completed the course DOE 1.
You can only use SAS JMP as statistical program on this course. Before the start of the course, you need to have SAS JMP downloaded on your own PC.
If you would like further focus on developing performance and processes, you can seek more methodical inspiration on the module Design for Six Sigma, DfSS or the module Quality Function Deployment, QFD, where the actual customer requirements for a new process are mapped operationalized and the building blocks for a future process are created.