Student guide Faculty of Engineering A.Y. 2008/09

The uncertainty and variability inherent in industrial problems calls for students to be familiar with statistical methods and probability calculation models. The course therefore provides an introduction to probabilistic modelling and its applications for the quantitative resolution of engineering-managerial problems.

In the first part, statistical methods are illustrated with particular reference to analysis of the data, estimation of parameters and linear regression, with application examples in to the fields of logistics and managerial forecasting.

The second part covers the fundamentals of stochastic computation. The Poisson processes and Markov processes are analysed, with examples of applications to the reliable modelling of systems and to models for inventory management and sales forecasting.

The teaching method adopted is intuitive, with an emphasis on the practical applications of the various methods illustrated.

Analysis of experimental data and statistics – Mathematical analysis I.

Fundamentals of probability theory

Multivariate probability calculation

Analysis of data:

-    Estimators

-    Method for estimating the maximum likelihood.

-    Other estimation methods.

-     Simple linear regression.

-   Multiple linear regression.

Poisson processes

Discrete Markov processes

Continuous Markov processes

Application to inventory management

Application to analysis of reliability and production

Borgonovo E., Metodi Probabilistici, Statistici e Processi Stocastici, Edizioni Cusl 2003

Kulkarni V., Modeling, Analysis, Design, and Control of Stochastic Systems (Springer Texts in Statistics),  Springer Verlag, 1999

Ross S.M., Stochastic Processes, John Wiley and Sons, 1996

Cifarelli D.M., Introduzione al Calcolo delle Probabilità, Mc-Graw Hill, 1998