Ateneo - UPO

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

Lecture notes will be provided by the lecturer. Further details will be given during the lessons and on the course website. Useful textbooks are:
- R. Johnson, D. Wichern. Applied Multivariate Statistical Analysis. VI Edition, Pearson
- G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning (with Application in R). Springer

- S. M. Iacus, G. Masarotto. Laboratorio di statistica con R, MacGraw Hill.
- P. Giudici. Data Mining. Metodi informatici, statistici e applicazioni. II Edizione, McGraw Hill

The goal of the course is to introduce the students into statistical techniques and their applications using the R statistical software. Methodologies will be illustrated throughout real case studies.

Fundamentals of mathematics and statistics.The 2 cfu course “ Metodi quantitativi per le decisioni” is warmly suggested.

Both theoretical and practical classes will be held in a computer lab. Assignments. Classes attendance is highly recommended.

Further informations (such as link to software website) can be found at the web page of the course: https://dir.uniupo.it/

Students with physical disabilities, Learning Disabilities or Special Education Needs can request
specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse

e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-
students-physical-or-learning-disabilities

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The exam is formed by a personal essay and an oral examination. The personal essay concerns a data analysis carried out using the statistical tools introduced during classes. Oral exam is a discussion about the essay and some theoretical questions.

1. Introduction to R. Programming R language. Generating data and data sources. First uni- and bi-variate statistical analysis and applications.
2. Introduction to Multivariate Statistics: data matrix, centroid, variance-corvariance matrix. Joint distributions and mixtures
3. Principal component analysis. Applications.
4. Cluster Analysis. Hierarchical clustering algorithms and the k-means method. Applications in Marketing.
5. Discriminant Analysis. Predicting Credit Risk of Small Businesses: the Z-score model. ROC curve.
6. Multivariate regression. Estimation techniques, goodness-of-fit, dummy variables, prediction. Market model regression, Production and Cost Function Estimation, Estimating Demand Functions.
7. Logistic regression. Applications.
8. Non parametric Regression methods: kernel, splines and smoothing splines.

The programme could be modified following the needs that will arise along the way.

Students will know and be able to use multivariate statistical tools: descriptive statistics, clustering, discriminant analysis, regression (linear, logistic, non linear), principal component analysis, etc. Students will be able to apply these tools to dataset by using the statistical software R.