SEP 767 / Multivariate Statistical Methods for Big Data Analysis and Process Improvement

3 unit(s)

(cross-listed as CHEM ENG 765)

This course is based around multivariate latent variable models which assume low dimensional latent variable structures for the data. Multivariate statistical methods including Principal Component Analysis (PCA), and Partial Least Squares (PLS) are used for the efficient extraction of information from large databases typically collected by on-line process computers. These models are used for the analysis of process problems, for on-line process monitoring, and for process improvement.