Multivariate Data Analysis

Multivariate Data Analysis

Number of days:3
Fee: € 1500
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The massive amounts of collected data, often just stored without being analysed, might contain valuable information about wanted and unwanted variation in process factors and product properties.
A multivariate approach allows combining multiple series of (non-designed) data. Taking into account all available information will lead to insights, such as the identification of parameters that have an impact on the quality of both chemical and biological products, which then provides directions towards quality improvement.

During day 1 qualitative aspects of multivariate data analysis will be treated: exploring the data, searching for correlations, clusters, outliers, ...
In day 2 we come to the model building part: searching for relations between groups of variables. Emphasis will be put on correctly selecting and applying the appropriate multivariate method, and on the correct interpretation of the results.
During this course the course matter will be immediately applied on real-life cases / exercises on PC.

Each participant is offered free individual follow-up coaching. Follow-up coaching means that each participant can appeal to the trainer’s expertise, after having applied the methods treated in the course to his / her own cases. This coaching comprises an individual follow-up session of two hours with the trainer, as well as follow-up support by phone.  Read more.

Multivariate analysis comprises a broad gamma of techniques to extract information from massive amounts of data, but at the same time contains an equally broad gamma of pitfalls. Breaking down the barriers towards multivariate analysis and smoothing the path towards expertise building, while at the same time making the participants aware of the problems that arise, are considered to be the main objectives of this course.
At the end of the course participants will be able to select the appropriate method to solve different kinds of problems, analyse the data and correctly interpret the results.

This course will be of great help to anyone who in daily practice is faced with large data tables and who is not familiar with the application of multivariate methods, and to those who have already been playing around with multivariate methods but don’t feel confident in the interpretation of multivariate graphs and numbers.
Prior knowledge is not required.


Day 1: Exploratory multivariate analysis

  • Visualisation of big datasets
  • Principal Component Analysis (PCA)
  • Cluster analysis: searching for groups of similar samples

Day 2: Quantitative analysis: in search of cause-effect relations

  • Multiple Linear Regression (MLR) with uncorrelated variables
  • Multiple Linear Regression (MLR) with correlated variables
    • Stepwise regression
    • The collinearity problem
    • An overview of the pitfalls
  • Principal Component Regression (PCR) 
  • Partial Least Squares (PLS) 
    • Interpretation of PCR and PLS models
    • Validation of regression models
    • Detection of outliers and non-linearities
    • Prediction with regression models
  • Some alternatives

Day 3: Quantitative analysis: the sequel + specific applications

  • Feasibility study: does a quantitative analysis make sense?
  • Classification (supervised pattern recognition): predicting class membership
    • Linear Discriminant Analysis (LDA)
    • Soft Independent Modeling of Class Analogy (SIMCA)
    • PLS-DA
  • Specific applications:
    • QSAR / QSPR (Quantitative Structure Activity / Property Relations)
    • Multivariate SPC (M-SPC)
    • Principal Properties Design
    • ......

Each course day will be held from 9 am to about 4.30 pm. The course fee includes handouts, lunches and the individual follow-up coaching.