| Type: | Open University
Language: English |
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Previous course: C0411
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No credit points with: C0411
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Prerequisite: 04040/C0410/04041/C0401
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Recommended semester: 4th -7th semester
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Scope and form: Lectures and tutorials.
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Examination: Written exam (13-scale)
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Remarks: The course is a general methodological course aimed at students interested in the analysis of multidimensional data or in achieving an overview over a number of the most common statistical methods.
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Department: Informatics and Mathematical Modelling
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Aim: To give the participants a more thorough understanding of statistical methods with special emphasis on revealing the structure of a multidimensional data. The participants are expected to learn to assess multidimensional (linear and non-linear) relations and estimate best predictors, to analyze the influence of complicated experimental designs, (uni- or multi- dimensional.) measurements, to assess whether multidimensional data can be reduced to lower dimensionality and in doing this, to assess whether a number of features in a population can be described with a few "factors", to discriminate between different populations using simple (linear) functions of measurements of different features of the single individuals, to separate a given set of data into relatively homogeneous classes, to assess the structure of and relations between phenomena which vary in time, and lastly to be able to use standard computer programs.
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Contents: Multidimensional models: Multidimensional distributions, multiple and partial correlation. The general linear model: Estimation and testing, geometric interpretation. Regression analysis: Estimation and testing, determination of best regression equations, analysis of residuals, prediction intervals, non-linear analysis etc. Analysis of variance: Crossed and hierarchical models, models with systematic and random error. Multidimensional. analysis of variance: Hotellings discriminators, test for discriminators of a given structure. Canonical analysis: Canonical correlation, principal components, factor analysis. Cluster analysis: Hierarchical and non-hierarchical methods. Correlation models: Models for random phenomena which vary in time and space.
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