MULTIVARIATE STATISTICAL ANALYSIS (DS3066)

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This course is designed to extend the statistical analysis of environmental and social science data: it will highlight the building blocks of multivariate analysis from the definition of the research problem to the interpretation of the results. Both dependence methods (that is in which one or several variables can be expressed in terms of the others – for instance Multivariate Analysis of Variance or Discriminant Analysis) and interdependence methods (where all the variables are analysed simultaneously – for instance Factor & Cluster Analyses or Multidimensional Scaling) will be studied.
Significant applications will be analysed and discussed so as to develop new insights.
Projects (individual or with peers), will allow the students to apply the multivariate models, thereby enhancing the importance of work and knowledge sharing.
Statistical software package: SPSS.
Prerequisite: MA 1020

Code: 
DS3066
Name: 
MULTIVARIATE STATISTICAL ANALYSIS
Discipline: 
DS (Data Science)
Type: 
Regular
Level: 
Undergraduate
Credits: 
4
Can be taken twice for credit?: 
No
Pre-requisites: 
MA1020 OR MA1020CCM OR MA1020GE120
Co-requisites: 
None
Equivalencies: 
MULTIVARIATE STATISTICAL ANALYSIS (MA3066)

Learning Outcomes

  1. Ability and fluency in using several statistical software packages.
  2. Be able to check the assumptions pertaining to each multivariate method (and be able to deal with them when they are not satisfied) and conduct a thorough multivariate analysis according to the process described in LO 1.
  3. Have knowledge of the specificity of each type of multivariate method and determine which one is appropriate for a given research problem.
  4. Understand the process of multivariate analysis from the definition of the research problem and the relevance of checking to the interpretation of the results.

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