Training Courses

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• The world is multivariate - Introduction to multivariate data modeling
• When are multivariate methods useful?
• Principles and applications of PCA, MLR, PCR...

• Data preprocessing
• PCA
• Multivariate regression
• Prediction
• Classification (SIMCA and PLS-DA)
• Multivariate Curve Resolution (MCR)

• Practical applications of multivariate techniques such as PCA, PCR, and PLS
• Non-mathematical approach - ideal for analysts with little or no background in statistics
• Step by step introduction of new concepts and techniques that promotes ease of learning
• Theory supported by practical hands-on exercises based on real-world data

• Introduction to experimental design
• Questions experimental design can answer
• Classical experimental strategies / Why is experimental design useful?

• The Unscrambler®, Multivariate analysis (MVA) & Spectroscopy overview
• Data Handling (Importing / Exporting)
• Visualizing Data through plots
• Inspection (Evaluation) of data quality

• Introduction: The world is multivariate
• Principal Component Analysis (PCA) – theory and applications
• Partial Least Squares Regression (PLS-R) – ...

• Univariate statistics
• Experimental setup
• Evaluation of panel data
• Principal Component Analysis (PCA)
• Varimax rotation
• Preference mapping

• Design of experiments
  • Efficient data collection
  • Screening designs
  • Optimization designs
  • Evolutionary Operation (EVOP) design