Tu sei qui: Home › Didattica › Corsi presso la sede di Novara › Programmi degli insegnamenti › 2015-2016 › Statistica applicata - eng

Statistica applicata - eng

STATISTICA APPLICATA

Dott. Enea Giuseppe Bongiorno
Prof. Aldo Goia
Dott.ssa Caterina May


Codice Insegnamento: EC0003
SSD Insegnamento: SECS-S/01
6 CFU – 48 ore
Sede: Novara




•    Lingua insegnamento  
Italian

•    Contenuti
Introduction to multivariate statistics (models and algorithms for classification and regression) through the use of statistical software R.

•    Testi di riferimento
Material provided by the teachers. Further details will be given during the lessons and on the course website.
Useful textbooks are:
- P. Giudici. Data Mining. Metodi informatici, statistici e applicazioni, MacGraw Hill.
- S. M. Iacus, G. Masarotto. Laboratorio di statistica con R, MacGraw Hill, Ultima edizione.


•    Obiettivi formativi
The goal of the course is to introduce the students into statistical techniques and their applications using some statistical packages. Topics will be illustrated throughout real case studies.


•    Prerequisiti
Fundamentals of mathematics and statistics.


•    Metodi didattici
Both theoretical and practical classes will be held in a computer lab. assignments. Frequency of lessons is highly recommended.


•    Altre informazioni
Further informations (such as link to software website) can be found in the web page of the course at the URL: https://eco.dir.unipmn.it/

•    Modalità di verifica dell’apprendimento
Written and oral examination. Oral exam is a discussion about the written exam and a short elaborate.


•    Programma esteso
Introduction to R.  Programming R language. Generating data and data sources. First uni- and bi-variate statistical analysis and applications.
Introduction to Multivariate Statistics: data matrix, centroid, variance-corvariance matrix. Mixtures.
Cluster Analysis. Hierarchical clustering algorithms and the k-means method. Applications in Marketing.
Discriminant Analysis. Predicting Credit Risk of Small Businesses: the Z-score model. ROC curve.
Multivariate regression. Estimation techniques, goodness-of-fit, dummy variables, prediction. Market model regression, Production and Cost Function Estimation, Estimating Demand Functions.
Logistic regression. Applications.