- Docente: Silvia Cagnone
- Crediti formativi: 6
- SSD: SECS-S/01
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
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Corso:
Laurea Magistrale in
Economics (cod. 8408)
Valido anche per Laurea Magistrale in Economics and Econometrics (cod. 5977)
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dal 13/02/2024 al 19/03/2024
Conoscenze e abilità da conseguire
At the end of the course the student has acquired knowledge of the multivariate methods for analyzing high dimensional data. In particular, he/she is able: - to interpret methods of dimension reduction including principal component analysis and factor analysis - to interpret methods of clustering and discrimination - to apply the proper multivariate method and perform his/her own analysis of high dimensional datasets using the software R.
Contenuti
- Multivariate and high dimensional problems. Basics of linear and matrix algebra. Random vectors and Gaussian random vectors.
- Principal component analysis: principal component method, visualising principal components, choosing the number of principal components.
- Factor analysis: factor model specification, identification,estimation, rotation, factor scores .
- Discriminant analysis: linear discriminant analyses, quadratic discriminant analysis, Fisher’s discriminant rule, linear discrimination for two normal populations and classes, evaluation of discriminant rules.
- Cluster analysis: distance and similaty measures, hierarchical Agglomerative Clustering, k-means Clustering
Prerequisite knowledge: Probability and Statistics
Testi/Bibliografia
Koch I. Analysis of Multivariate and High Dimensional Data, Cambridge University Press, 2014
Metodi didattici
Lectures and tutorials with the software R
Modalità di verifica e valutazione dell'apprendimento
The exam consists of a final project on real data analysed with the software R on one or combined topics of the course and some theoretical questions. The oral exam is optional. The final project must be sent to the teacher one week before the date of the exam. The oral exam consists of theoretical questions and allows the student to increase or decrease the grade obtained in the project. In this case the final grade is the average between the grade of the project and oral exam.
The grade can be rejected at most once.
Grading scale
< 18: failed
18-23: sufficient
24-27: good
28-30: very good
30 e lode: outstanding
Strumenti a supporto della didattica
Teacher's note available at https://virtuale.unibo.it/
Orario di ricevimento
Consulta il sito web di Silvia Cagnone