Building predictive models for spectral data using functional data analysis and experimental designIn recent years, partial least squares (PLS) has been used to build predictive models for spectral data. A newer approach using Functional Data Explorer and experimental covariate design allows for the use of fewer spectra in predictive models with good predictive power. This approach uses one-quarter to one-third of the data that would otherwise be used to build predictive models based on spectral data. New multivariate platforms such as model-driven multivariate control charts (MDMCC) will also be presented as ways to enhance the analysis of spectral data.
Advanced Statistical ModelingBasic Data Analysis and ModelingConsumer and Market ResearchData Exploration and VisualizationDesign of ExperimentsPredictive Modeling and Machine LearningQuality and Process Engineering
