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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2022/2023
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052911 - APPLIED STATISTICS
Docente Secchi Piercesare
Cfu 5.00 Tipo insegnamento Monodisciplinare
Didattica innovativa L'insegnamento prevede  1.0  CFU erogati con Didattica Innovativa come segue:
  • Blended Learning & Flipped Classroom

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Des (Mag.)(ord. 270) - BV (1092) DESIGN DEGLI INTERNI - INTERIOR DESIGN*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1097) DESIGN FOR THE FASHION SYSTEM - DESIGN PER IL SISTEMA MODA*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1159) PRODUCT SERVICE SYSTEM DESIGN - DESIGN PER IL SISTEMA PRODOTTO SERVIZIO*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1160) DESIGN DEL PRODOTTO PER L'INNOVAZIONE*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1162) DESIGN DELLA COMUNICAZIONE*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1163) DESIGN PER IL SISTEMA MODA*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1164) PRODUCT SERVICE SYSTEM DESIGN*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1260) INTERIOR AND SPATIAL DESIGN*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1261) INTEGRATED PRODUCT DESIGN*AZZZZ053791 - APPLIED STATISTICS
Des (Mag.)(ord. 270) - BV (1262) DIGITAL AND INTERACTION DESIGN*AZZZZ053791 - APPLIED STATISTICS
Ing Ind - Inf (Mag.)(ord. 270) - BV (479) MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE*AZZZZ099433 - APPLIED STATISTICS FOR ENG4SD
052911 - APPLIED STATISTICS

Obiettivi dell'insegnamento

The course covers advanced approaches in the areas of statistical modeling and data analysis, using ideas that bridge the gap between statistics and computer science and developing tools for the statistical mining of data. The focus is on predictive learning, with particular emphasis on recent advances in regression.

The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:

  • Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment
  • Interact in a professional, responsible, effective and constructive way in a working environment, also motivating group members

Risultati di apprendimento attesi

- At the end of the course students are expected to be able to design and run with R a data driven analysis, aimed at interpretation or prediction, by fitting a regression model when heteroscedasticity or dependence among the observation is a relevant issue. Special focus will be deserved to linear models with heterogenous variance, to fixed effects linear models for correlated data, to mixed-effects linear models and to kriging, when dependence is spatial. Overtures to dynamic linear models and Markov models will complement their tools for statistical data exploration and prediction. Leveraging on their engineering forma mentis and on the skills in data analysis acquired in the course, students are expected to be able to evaluate the practical and statistical significance of the final result of their data analysis, to quantify its uncertainty and to diagnose its potential shortcomings, either when used to provide an empirical explanation of the industrial or scientific problem under study or when its main goal is to formulate predictions.  

 

- To prepare for responsible and efficient interactions in a working environment, every student is required to take part in a real data analysis project developed by an independently formed team of 2-4 members. The work in progress of the projects will be collectively discussed during general meetings scheduled along the course; final analyses and results will be presented in a workshop which will take place at the end of the course.


Argomenti trattati

Program:

  • Introduction: a short recap on linear models for independent data
  • The case of heterogeneous variance
  • Linear fixed-effects models for correlated data
  • Linear mixed-effects models
  • Spatial statistics: kriging
  • A brief introduction to Dynamic Linear Models and Kalman filtering
  • A brief introduction to Markov Models and Hidden Markov Models

All methods will be illustrated using applications from marketing, finance, biology and other areas; the R free software environment for statistical computing and graphics (downloadable at www.r-project.org ) will be used throughout the course and its lab sessions.

Through the course, students are required to work in team on a real data analysis project whose progress will be shown periodically to the class.

 

 


Prerequisiti

Business Data Analytics


Modalità di valutazione

The exam consists of two parts:

(a) A written exam. The written exam consists of a few (usually three or four) data analysis problems to be solved with R. The exam is open book: the use of a personal computer is allowed as well as that of books, personal notes etc.

(b) Team Project evaluation. Projects will be collectively evaluated by the teachers of the course and by the students participating to a final workshop at the end of the course. 

To pass the exam the student must pass each part of the exam with a score greater than or equal to 18/30; the final score is then obtained as the weighted average of the two scores, with weights respectively equal to 0.6 for the written exam and 0.4 for the project evaluation.


Bibliografia
Risorsa bibliografica obbligatoriaGalecki A. e Burzykowski T, Linear Mixed-Effects Models using R, Editore: Springer, Anno edizione: 2013
Risorsa bibliografica facoltativaCressie N., Statistics for Spatial Data (Revised Edition), Editore: Wiley, Anno edizione: 1993

Software utilizzato
Nessun software richiesto

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
25:00
37:30
Esercitazione
0:00
0:00
Laboratorio Informatico
25:00
37:30
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.8.0 / 1.8.0
Area Servizi ICT
03/12/2022