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Multivariate Data Analysis - I

The world is multivariate - Introduction to multivariate data modeling

  • When are multivariate methods useful?
  • Principles and applications of:
    • Principal Component Analysis (PCA)
    • Multivariate regression: Multilinear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS)
  • Relevant data collection
  • Multivariate modeling step by step
  • Pretreatment and scaling
  • Detecting and dealing with outliers
  • Calibration, validation
  • Prediction
  • The different validation methods
  • Basic rules for successful data analysis

Who should participate in this program?

The courses have been designed for individuals:

  • Involved in consumer insights, R&D, product development, process optimization, quality control & monitoring.
  • Working with spectroscopic instruments (NIR, FTIR, UV, UV/VIS, NMR, DAS, Raman, Mass Spectroscopy) chromatography instruments (LC, CE, GC, HPLC), production data & sensory data, R&D, quality control or production processes.

No prior knowledge of The Unscrambler® is required to attend our courses.

Prerequisites

  • The number of seats for the course is limited to 12
  • Deadline for registrations: 3 weeks before the course-start
  • Participants are required to bring their own Laptop.

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