Multivariate Analysis 2600-ABz1AWkf
1. Introduction to Multidimensional Data Analysis
• Characteristics of data sets, high-dimensional problems, standardization of variables.
2. Multiple Regression and MANOVA
• Applications in business research (e.g., factors influencing customer satisfaction).
3. Principal Component Analysis (PCA)
• Dimensionality reduction, visualization, applications in market segmentation.
4. Factor Analysis
• Latent factors, interpretation, applications in preference research.
5. Clustering
• Hierarchical and non-hierarchical methods, customer segmentation.
6. Discriminant Analysis
• Object classification, comparison with machine learning methods.
7. Correspondence Analysis and Exploratory Methods
• Applications in consumer preference analysis and marketing research.
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Term 2025Z:
1. Introduction to Multidimensional Data Analysis |
Term 2026Z:
1. Introduction to Multidimensional Data Analysis |
Type of course
Learning outcomes
Upon completion of the course, the student:
In terms of knowledge:
• Knows basic multivariate analysis methods, their assumptions, and limitations (K_W01),
• Understands the application of methods in business and marketing analyses (K_W05).
In terms of skills:
• Is able to select the appropriate tool to solve an analytical problem (K_U01),
• Performs multivariate analyses using computer software (K_U03),
• Interprets and presents analysis results in a business context (K_U09).
In terms of social skills:
• Understands the importance of reliable data analysis for decision-making processes,
• Is able to work in a team on an analytical project.
Assessment criteria
Final Test
Number of points/grade
Passed by more than 50% of points
Practical placement
Internship is not required to complete the course.
Bibliography
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Term 2025Z:
1.Aczel A.D. (2000). Statystyka w zarządzaniu. PWN.. |
Term 2026Z:
1.Aczel A.D. (2000). Statystyka w zarządzaniu. PWN.. |