Quantitative Methods

A.Y. 2019/2020
12
Max ECTS
80
Overall hours
SSD
SECS-S/01 SECS-S/03
Language
English
Learning objectives
The aim of this course is to provide students with practical and theoretical understanding of some of the most used multivariate statistical methods, with a particular attention to techniques useful for business and marketing applications. More specifically, the scope of the course is to give the students the necessary tools to be able to deal with simple and complex problems that a company may be facing, by using information and statistical methods suitable for the purpose, such as regression analysis, cluster analysis or principal component analysis, among the others.
Expected learning outcomes
At the end of the course, students will be able to represent a dataset through tables and graphs, to summarise the information using descriptive statistics, by appropriately considering eventual outliers. Using the SPSS software, students will be able to apply the appropriate quantitative tools on various real-data scenarios, and to give a correct interpretation and an adequate representation of the result. Specifically, they will be able to choose the statistical tool suitable to a specific problem, they will learn to select a regression model for a response (dependent) variable, given a set of covariates, to estimate the parameters of the model and to use tests of hypotheses in order to answer a research question or to take decisions.
They will put in practice the use of advanced descriptive tools, such as cluster analysis or principal component analysis, aimed at detecting the existence of homogeneous groups of observations or to synthesise the total information in a small number of "factors".
Course syllabus and organization

Unique edition

Responsible
Lesson period
Second trimester
Prerequisites for admission
Students must be acquainted with a basic course in statistics (descriptive and inferential)
Assessement methods and criteria
The exam is a composed of two parts: one part consists in theoretical questions (up to 20% of the grade), the second part consists in
Computer exercises to be solved using SPSS.
Module 1
Course syllabus
Review of measurement, scale, data, inference. Bivariate analysis and dependence. Simple and multiple regression model, nonlinear transformations of variables.
Teaching methods
Lectures are divided (approximately 50-50) into traditional classes, where the theory is introduced, and laboratories, where the theory is put in practice through the use of a statistical package (SPSS)
Bibliography
M. Mazzocchi, Statistics for Marketing and Consumer Research, Sage Publications, 2008
Module 2
Course syllabus
Principal component analysis, cluster analysis, discriminant analysis, logit and probit models
Teaching methods
Lectures are divided (approximately 50-50) into traditional classes, where the theory is introduced, and laboratories, where the theory is put in practice through the use of a statistical package (SPSS)
Bibliography
M. Mazzocchi, Statistics for Marketing and Consumer Research, Sage Publications, 2008
Module 1
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Module 2
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 40 hours
Professor: Leorato Samantha
Educational website(s)
Professor(s)
Reception:
Wednesday 13:30 -16:30. Until the continuation of the COVID-19 emergency measures, the office hours will be arranged via conference call. Students who need an appointment are invited to contact me by email to arrange an e-meeting
Room 32 DEMM