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Academic Year 2018/2019 - 1° Year - Curriculum APPLICATIVO, Curriculum DIDATTICO and Curriculum TEORICO
Teaching Staff: Antonio PUNZO
Credit Value: 6
Taught classes: 35 hours
Exercise: 12 hours
Term / Semester:

Learning Objectives

  1. Knowledge and understanding: The course aims to provide the basic tools for statistical modelling and data analysis.
  2. Applying knowledge and understanding: Based on the knowledge gained, the student will be able to use the main statistical methods to analyze and investigate key aspects of data of interest.
  3. Making judgements: the student will be able to collect, process and interpret quantitative and qualitative data.
  4. Communication skills: The student will be able to transfer to others, with mastery of technical language, information and assessments relating to distributions of data.
  5. Learning skills: At the end of the course the student will have acquired the necessary knowledge to be able to continue its studies in the statistical modelling framework. Learning is achieved through a gradual process in close connection with disciplinary issues and with the educational objectives specific to the Degree Course in Mathematics.

Course Structure

Lectures via slides. A statistical software will be also used.

Detailed Course Content

Unidimensional Analyses. Decriptive and inferential statistics. Nonparametric density estimation via kernel smoothing techniques.

Multidimensional Analyses. Cluster analysis. Finite mixture models. Model selection criteria.

Regression models. Univariate and multivariate linear regression. Least square estimates. Goodness-of-fit measures. Model diagnistick checking. Inference on the parameters of the regression model. Generalized linear model. Nonparametric regression.

Textbook Information

  1. Zenga, M. (1995), Modello Probabilistico e Variabili Casuali, Giappichelli Editore, Torino
  2. Zenga, M. (1996), Inferenza Statistica, Giappichelli Editore, Torino
  3. Zenga, M. (2007), Lezioni di Statistica Descrittiva, Giappichelli Editore, Torino
  4. Ricci, V. (2006), Principali tecniche di regressione con R, disponibile al link
  5. Iacus, S. M. e Masarotto, G. (2007), Laboratorio di Statistica con R, McGraw-Hill