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| Detailed information |
| Pre-requisites |
(*)keine
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| Original study plan |
Bachelor's programme Statistics and Data Science 2025W |
| Learning Outcomes |
Competences |
| Students know the methods of cluster analysis, factor analysis, structural equation modelling, LISREL and can apply them to data.
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| Skills |
Knowledge |
- Knowing and understanding the basic problems, terms and methods of cluster analysis (k1, k2)
- Knowing and understanding the basic problems, terms and methods of factor analysis and structural equation modelling (k1, k2).
- Applying, assessing and comparing factor analysis methods (k3, k4, k5)
- Applying factor analysis methods with the freeware R (k3).
- Specifying simple structural equation models (k2, k3).
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- Cluster analysis
- Hierarchical agglomerative and divisive methods
- Factor analysis: Principle component analysis, common factor analysis, factor rotation (orthogonal/oblique), estimating factor scores
- Structural equation modelling: Confirmatory factor analysis, LISREL models (structure and measurement model), path diagrams, parameter estimation
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| Criteria for evaluation |
homework and written exam
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| Methods |
presentation by the lecturer
presentation of the homework by students and discussion
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| Language |
German |
| Study material |
Skriptum
Fahrmeir, Hamerle, Tutz, Multivariate statistische Verfahren.
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| Changing subject? |
No |
| Corresponding lecture |
(*)ist gemeinsam mit 551OKMEVLMK14: KV Verallgemeinerte Lineare Modelle (4 ECTS) äquivalent zu
4MSMV1KV: Multivariate Verfahren I (8 ECTS)
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