### Teachings

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Professor
SILVIA COLUMBU (Tit.)
Period
Second Semester
Teaching style
Convenzionale
Lingua Insegnamento
ITALIANO

Informazioni aggiuntive

Course Curriculum CFU Length(h)
[60/65]  MATHEMATICS [65/20 - Ord. 2012]  Applicativo 6 48

### Objectives

OBJECTIVES

Provide the concepts and the mathematical theory of the statistical modelization to make the student able to implement a complete a rigorous statistical analysis, give to the students the practical tools to implement the methodologies presented in order to be able to collaborate with research institutes and/or with companies working with the data analysis.

1) KNOWLEDGE AND UNDERSTANDING:
Ability to formalize and to model problems of statistical inference.

2) APPLYING KNOWLEDGE AND UNDERSTANDING:
Be able, through the use of suitable softwares, to develop a complete data analysis: data acquisition, data description and their statistical modelization.

3) MAKING JUDGEMENTS:
Be able to interprete the outputs and the results of a statistical analysis, and to reformulate the same analysis if required.

5) COMMUNICATION SKILLS:
The student, at the end of the course, should be able to communicate the results of a rigorous statistical analysis. The student should be put in the condition to be able to collaborate and participate to applied statistical research, not only in the mathematical field.

6) LEARNING SKILLS:
The course, with the aid of a large number of practical applications using the statistical software R, aims to give the students the tools to solve problems of descriptive and inferential statistics. The student will have the instruments to understand and experiment the application of other statistical methods, apart from those teached during the course, based on the available data.

### Prerequisites

It is necessary for the student to know the basis of probability calculus and the descriptive and inferential methods of mathematical statistics. The student should also know the basis of linear algebra and the principal instruments of matrix calculus.

### Teaching Methods

The course consists of theoretical lectures on the mathematical definition of models and practical lectures to understand how to apply the models to the analysis of data using the statistical software R.

### Verification of learning

The final exam is in two stages. 1) The student must write a statistical report of an analysis implemented in R with real data. In the analysis the student must apply the appropriate models explained during the lectures on the basis of the characteristics of the data. 2) An oral exam to verify the knowledge of the mathematical theory related to the different methods studied.

### Texts

Topics will be exposed using the following text book:

-Ornello Vitali (1993). Statistica per le Scienze Applicate: volume 1 e 2. Cacucci Editore.

-P. McCullagh and J.A.Nelder (1989). Generalized linear Models. Chapman & Hall/CRC.

-R. Koenker (2005). Quantile regression. Cambridge University Press.

For the computational part with R we recommend:

1. An Introduction to R: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
2. Il linguaggio R: concetti introduttivi ed esempi (II edizione) by Vito M. R. Muggeo and Giancarlo Ferrara: https://cran.r-project.org/doc/contrib/nozioniR.pdf

Other materials will be provided by the instructor.

Other text books related with the course are:

- G. Casella, R. L. Berger (2002). Statistical inference, (2nd edn.), Wadsworth Group, CA, USA.