COURSE OBJECTIVE
After completing the course, participants will know how to:
• Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data
• Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling
• Interpret models to gain scientific insights
• Learn how to construct predictive models and apply them in practice. Use multivariate calibration to predict and improve quality
• Classification of raw materials and ingredients
• Analysing a multivariate problem.
WHO SHOULD PARTICIPATE?
The course is intended for researchers, scientists and engineers from all sectors of industry and academia. No prior knowledge of statistics is assumed.
SELECTED COURSE CONTENT
After completing the course, participants will know how to:
• Apply PCA (Principal Component Analysis) to detect outliers, trends, patterns and to classify groups within complex data
• Relate multiple responses to multiple inputs using PLS (Partial Least Squares) modelling
• Interpret models to gain scientific insights
• Learn how to construct predictive models and apply them in practice. Use multivariate calibration to predict and improve quality
• Classification of raw materials and ingredients
• Analysing a multivariate problem.