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Multivariate Data Analysis in Environmental Science

Field expedition during PhD course implementation January 2013

Partner University: Sokoine University of Agriculture (SUA)

Teaching team: Ass. Prof. Anne Mette Lykke, Prof. Anders Sanchez Barfod (Aarhus University), Prof. Philibert Ndunguru (SUA)

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Timing and Duration: 7-13 January, 2013.

Venue: ICE SUA

Course objective: To provide an integrated, in depth, but applied approach to multivariate data analysis and linear statistical models in environmental science research. There will be a strong emphasis throughout the course on graphical methods for visualizing data and the results of statistical models.

Course content: The statistical topics to be covered will include:

  • Regression analysis
  • Univariate and multivariate ANOVA and ANCOVA
  • Discriminant analysis
  • Canonical correlation analysis
  • Principal components analysis
  • Cluster analysis, Multidimensional Scaling and/or Logistic regression

Most of these methods are actually special cases of the General Linear Model. By developing these techniques within this framework, PhD students will appreciate the conceptual unity under-lying all forms of regression and all analysis of variance designs, both univariate and multivariate.  This unification of these seemingly different forms of analysis will be achieved through use of matrix algebra to formulate the various models. Therefore, the first part of the course will be devoted to the necessary mathematical skills. In order to facilitate exercises and homework problems which involve matrix operations, students will be given instruction in using a computer package for matrix algebra.

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