- Introduction to multivariate data analysis Explore and discuss the multivariate analysis
- Discuss the concept of latent variables
- Review relevance to Industrial problems
- Principal Component AnalysisDiscuss the concept of Principal Component Analysis and the advantages to handle multivariate data
- Apply PCA to a standard dataset and interpret results
- Interpret residual plots to estimate the number of components needed
- Workshop: Analyze a Regional Food Dataset Using PCA
- Workshop: Analyze the Quality of a Polymer Product Using PCA
- Workshop: Analyze a Silicon Wafer Process Using PCA
- Partial Least Squares, or Projection to Latent Structures (PLS) AnalysisDiscuss the advantages of Principal Component Regression and PLS over Multiple Linear Regression
- Develop PLS models that explain variability in X and correlate to Y
- Review how PLS can be implemented to solve various industrial problems
- Workshop: Analyze the LDPE Polymer Process Using PLS
- Historical Data Analysis and Multivariate Monitoring (MSPC)Conduct Historical Data Analysis for a PCA model
- Develop multivariate SPC charts to identify special causes of variation
- Perform Multivariate Statistical Process Control for online data
- Explore how MSPC can optimize a process by reviewing a case study
- Workshop: Monitor the LDPE Process Using MSPC
- Empirical Models Built from Historical DataReview what constitutes a useful empirical model
- Identify the differences between latent variable models and those developed from regression and machine learning methods
- Discuss the limitations of empirical models developed from historical data
- Workshop: Optimize the LDPE Process Using the Model Optimizer
- Soft Sensors (Inferential Models)Review the concept of soft sensors
- Discuss the performance of these models very poorly
- Workshop: Build a Dynamic PLS Model for the Kamyr Digester
- ClassificationReview the concept of classification
- Discuss how PCA and PLS methods can be used effectively to classify data
- Identify the industrial cases for classification
- Workshop: Build PCA and PLS classification models on the Iris dataset
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