MULTIVARIATE STATISTICS
Academic year and teacher
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- Versione italiana
- Academic year
- 2021/2022
- Teacher
- VALENTINA MINI
- Credits
- 6
- Didactic period
- Primo Semestre
- SSD
- MAT/06
Training objectives
- Knowledge of the theoretical bases and understanding of the methodological developments related to the analysis of multidimensional data. Familiarity and autonomy in the application of the main multivariate methods with the aid of the R.
Prerequisites
- Linear Algebra
Course programme
- Overview (introduction to the course, subject and software) [2h]; Vectors and matrices [3h]; Linear regression and multivariate regression: theory, analysis and applications in R [10h]; Interdependence analysis: presentation of techniques, theory, exercises and applications in R [16h]; Dependency analysis: overview [3h]; Test [6h]; Final exercises [2h].
Didactic methods
- Frontal lesson, exercises and laboratory using R.
In consideration of the restrictions related to the Covid-19 pandemic, the structure and modality of the lessons may vary. The use of computer and digital media will be oriented towards sharing the theoretical and practical parts and exercises. Learning assessment procedures
- Final written exam based on three sections:
- a theoretical part (multiple choice)
- a part of applied multivariate statistics (multiple choice)
- an exercise based on R environment (it is required to write the sequence of commands necessary to correctly perform the required analysis).
In consideration of the restrictions related to the Covid-19 pandemic, the examination method could be on electronic support (Google Forms) while still guaranteeing the written test divided into key parts: theory, practice, application in R. Reference texts
- The main materials are the course slides, suggested readings and exercises.
you will use the guide to use the software R.
During the lessons the professor will indicate specific texts related to the covered topics.
