Multivariate Data Analysis (MVDA) Open Onsite Course
This is an open onsite Multivariate Analysis (MVA) course that provides an overview of Principal Component Analysis (PCA) and Orthogonal Partial Least Squares, OPLS® using SIMCA®. A basic understanding of SIMCA® or multivariate statistics is beneficial but not needed. Participants will learn how to build multivariate process models using the latest multivariate techniques. The course comprises lectures, demonstrations, and computer exercises in SIMCA® based on real-life datasets.
Please register via "Request a quote”.
Course Objective
The objective of this course is to guide the participants through their journey from a process data set to a multivariate monitoring model based on their process data. Through this course, multivariate data tables are translated into interpretable charts and plots, which simplifies the process monitoring and analysis of process deviations. Multivariate technology is the science of separating the signal from the noise in data containing many variables and presenting the results in a simple graphical format. This allows users to quickly go from a complicated table of numbers to a simple plot of the essentials. The key to unlocking the information in any data set lies in the correlations among the variables, not in the variables themselves.
Who Should Participate?
The course is intended for researchers, scientists, and engineers from all sectors of industry and academia who have process understanding, but no or limited statistical background. Typical applications include product development, process improvement/optimization, deviation investigations, and scale-up of processes. Prior knowledge of statistics is beneficial but not needed.