Course 2005-2006 a.y.



Department of Decision Sciences

Course taught in English

Go to class group/s: 31
CLEA (6 credits - II sem. - AI) - CLAPI (6 credits - II sem. - AI) - CLEFIN (6 credits - II sem. - AI) - CLELI (6 credits - II sem. - AI) - CLEACC (6 credits - II sem. - AI) - DES (6 credits - II sem. - AI) - CLEMIT (6 credits - II sem. - AI) - DIEM (6 credits - II sem. - RR) - CLSG (6 credits - II sem. - AI)
Course Director:

Classes: 31 (II sem.)

Course Objectives

The purpose of the course is to enable students to structure and conduct autonomously a research project based on the analysis of data sets concerning business, economics and in general the social sciences. The course presents a set of tools with an applied perspective, providing the methodological knowledge that is necessary to conduct such project with a fair level of competence and with the ability to choose appropriate statistical methods for various problems. Lectures providing motivation, methods and examples are alternated with applied workshops (held in the computer lab) in which students actively participate. The lectures supply the students with the basic concepts and techniques of multivariate data analysis, which are then be linked to applications and data sets. Lectures and tutorials are also scheduled in order to introduce the students to the use of the widely-used package SPSS for the analysis of multivariate data (although alternative packages, including freeware packages, are discussed).

Course Content Summary


  • Structure of a research project based on the analysis of economic and business data
  • Types of data collection (i.e. census methods, sample surveys, cross-sectional and longitudinal data)
  • Sample surveys: some notes on sampling (i.e. stratification, two-stage sampling), some examples of questionnaires
  • Searching for data. Publicly available data (focus on the web) and restricted-access data
  • Searching for references and examples. 

Analysis of Dependence

  • Regression analysis  (i.e. Analysis and forecasts of pricing schedules)
  • Analysis of Variance (i.e. Analysis of effects of advertising campaign on different subgroups of consumers)
  • Models for limited dependent variables Logit Models (i.e. Entry decisions; choice models)

Analysis of Interdependence 

  • Principal Components Analysis (i.e. Reduction of complex data)
  • Factor Analysis (i.e. Social/status component vs. individual component as functions of observable purchasing characteristics)
  • Cluster Analysis (i.e. Classification of consumers in groups according to observable characteristics)

Detailed Description of Assessment Methods

Students will have to take a written exam and also hand in a workbook with the collection of applications done in the computer room, duly commented.


  • A. FIELD, Discovering Statistics, Sage.
  • Other notes and lectures to be specified with the detailed syllabus.
Exam textbooks & Online Articles (check availability at the Library)
Last change 04/05/2005 00:00