Chemometrics without Equations (or Hardly Any)
Chemometrics without Equations concentrates on two areas of chemometrics: 1) exploratory data analysis and pattern recognition, and 2) regression. Participants will learn to safely apply techniques such as Principal Components Analysis (PCA), Principal Components Regression (PCR), and Partial Least Squares (PLS) Regression. Examples will include problems drawn from process monitoring and quality control, predicting product properties, and others. The target audience includes those who collect and/or manage large amounts of data that is multivariate in nature. This includes bench chemists, process engineers, and managers who would like to extract the maximum information possible from their measurements.
Chemometrics Without Equations (or Hardly Any) is designed for those who wish to explore the problem solving power of chemometric tools, but are discouraged by the high level of mathematics found in many software manuals and texts. Course emphasis is on proper application and interpretation of chemometric methods as applied to real-life problems. The objective is to teach in the simplest way possible so that participants will be better chemometrics practitioners and managers.
1.1 what is chemometrics?
2 Pattern Recognition Motivation
2.1 what is pattern recognition?
2.2 relevant measurements
2.3 some statistical definitions
3. Principal Components Analysis
3.1 what is PCA?
3.2 scores and loadings
3.4 supervised and unsupervised pattern recognition
4.1 what is regression?
4.2 classical least squares (CLS)
4.3 inverse least squares (ILS)
4.4 principal components regression (PCR)
4.5 partial least squares regression (PLS)