Quantitative Methods
A.Y. 2019/2020
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".
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".
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Prerequisites for admission
Students must be acquainted with a basic course in statistics (descriptive and inferential)
Assessment 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.
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)
Teaching Resources
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)
Teaching Resources
M. Mazzocchi, Statistics for Marketing and Consumer Research, Sage Publications, 2008
Module 1
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professors:
Leorato Samantha, Tommasi Chiara
Shifts:
Module 2
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 40 hours
Professor:
Leorato Samantha
Shifts:
-
Professor:
Leorato SamanthaProfessor(s)
Reception:
Next office hours: Thursday 17.04 online only (send an email to request an appointment); Thursday 24.04 from 9:30 to 12:30, online only (send an email to request an appointment); Tuesday 29.04 from 10:30 to 12, Wednesday 30.04 from 14 to 15:30
Room 32 third floor
Reception:
Wednesday from 9:00 to 12:00
Via Conservatorio, III floor, Room n. 35