41301 - Statistical Inference

Course Unit Page

Academic Year 2018/2019

Learning outcomes

At the end of the course, the candidate has a solid knowlege of basic statistical inference and has some foundational knowledge about some of the main statistical techniques of data analysis, even when the number of variables is large. In particular, he/she is able to perform many parametric statistical tests, as well as the most used nonparametric tests, is able to get an estimator and to check its properties. He/she is familiar with the foundamental concepts of linear regression model. Besides the student is master of Factor Anaysis for quanitative and categorical variables.

 

Course contents

Part 1 (CARLO TRIVISANO)

(The following  contents refer also to the crash course of STATISTICS of the  two year Master, Financial and Actuarial ScincesFinancial and Actuarial Statistics)
- Introduction to statistical inference.
- Sample space, statistics and estimators.
- Finite and asymptotic properties of estimators.
- Methods of estimation.
- Hypothesis testing.
- Statistical tests for mean value, variance and frequency.
- Tests of goodness of fit and independence.

 

Part 2 (STEFANIA MIGNANI)
- Simple linear regression model: descriptive and inferential aspects
- Multiple regression model: descriptive and inferential aspects
- Multiple regression model: methods for selecting a subset of independent variables

- Multiple Regression: Model Validation and Diagnostics

- Introduction to latent variable models

- The factor analysis for quantitative variables

Readings/Bibliography

D. Piccolo, Statistica per le decisoni, Il Mulino, Bologna, 2010, PARTE III
Ulteriore materiale didattico è disponibile al sito https://www.unibo.it/sitoweb/carlo.trivisano.

PARTE B
- S. Mignani, A. Montanari, Appunti di analisi statistica multivariata, Esculapio, Bologna, Seconda edizione, 1997,
- D. Piccolo, Statistica per le decisoni, Il Mulino, Bologna, 2010, Capitolo XVIII.

Some additional readings will be found at AMS Campus

Teaching methods

Each topic covered in the lectures will be followed by exercises in practical classes Using R and SPSS software

Assessment methods

PART 1
Written test

PART 2
Oral examination

Teaching tools

Slides, books and papers

Office hours

See the website of Stefania Mignani

See the website of Carlo Trivisano