Multivariate Analysis

online 2 days

Most popular methods of multivariate data analysis are presented in the course. They can be used to analyse complex phenomena characterized by many variables. The course covers graphic presentations of multivariate data, applied multivariate regression, ordering of multidimensional objects, cluster analysis, basics of Factor Ananlysis and Principal Component Analysis and Multidimentional Scaling – all of them performed in Statistica Software.

For whom?

For those who are interested in the analysis of phenomena described by many variables with application in medicine, social sciences, economics, market research, psychology and many other fields.

As the results of the course you will know how to:

  • use the most popular techniques of multivariate data analysis
  • extract useful knowledge from your data
  • graphically present multivariate data
  • select the best subset of variables and build your own model
  • look for relations between variables
  • build rankings of objects described by many variables
  • reduce dimensiality of your problem
  • find groups of similar objects
  • be more fluent in Statistica

  • Program
  • Speaker
  • Organizational information
  • Prices

Training programme:

  1. Introduction
    • Measurement scales
    • Multivariate random variable. Finite mixture distribution.
    • Statistical hypotheses testing
  2. Graphical presentation of multivariate data
  3. Applied multiple regression
    • Linear multiple regression model
    • Essential steps in applied regression analysis
    • Stepwise regression
    • Goodness-of-fit measures
    • Analysis of residuals
  4. Linear ordering of multidimentional objects (rankings)
    • Identification and selection od diagnostic variables
    • Normalization and standarization
    • Aggregation formulas
  5. Cluster analysis
    • Tasks and problems in cluster analysis
    • Distance measures
    • Agglomerative methods
    • K-means method
    • Comparing partitions
    • Dynamic cluster analysis
  6. Multidimentional scaling
  7. Factor analysis and Principal Component Analysis
    • General ideas and aims of FA and PCA
    • Interpretation of eigenvalues and factor loadings
    • Identification of factors

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