AIMS AND CONTENT

AIMS AND LEARNING OUTCOMES

The learning objectives that will be evaluated for the purpose of passing the final exam are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics; knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation, hypothesis testing and regression analysis problems.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; be able to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to carry out dependence/independence and regression analyses, also in the inferential context; know how to read statistical analyses carried out with the methodologies presented in the course.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.

TEACHING METHODS

Lectures 75% Video, 6 hours of i-tivity and 25% classroom lesson.  Since the training objectives concern both theoretical and applicative skills, the lessons focused on methodological aspects of statistics will be alternated with exercises in which numerical problems and examples of simple analyses on real data are addressed.

SYLLABUS/CONTENT

Regressione Logistica

Regression Trees

Classification Trees

Hierarchical Clustering

K-means Clustering

RECOMMENDED READING/BIBLIOGRAPHY

Trevor Hastie, Robert Tibshirani, Jerome Friedman. "The Elements of Statistical Learning. Data Mining, Inference, and Prediction." Springer

TEACHERS AND EXAM BOARD

Exam Board

ANDREA CIACCI

STEFANO BRACCO (President Substitute)

STEFANO MASSUCCO (President Substitute)

MARTA NAI RUSCONE (President Substitute)

LESSONS

LESSONS START

Classes will start in the first week of the second semester according to the calendar of the Department of Economics.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists in a written assignment.

The examination regulations are published on the course's Aulaweb page.

ASSESSMENT METHODS

The questions and exercises of the written test are chosen to cover, as far as possible, all the topics of the exam program. In addition to the degree of understanding and the ability to apply knowledge, the correct use of the technical language of the discipline and the ability to read and correctly interpret statistical analyses constitute evaluation parameters.