Quantitative Methods
A.Y. 2024/2025
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 relevant information using descriptive statistics, by appropriately considering eventual outliers.
Students will be acquainted with statistical models, their theoretical foundations and their correct use and interpretation.
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"." Through the introduction of the statistical software R, students will learn to apply the appropriate quantitative tools on various real-data scenarios and an adequate representation of the results. As part of their final exam, they will also be able to design and develop an "empirical exercise" on their own.
Students will be acquainted with statistical models, their theoretical foundations and their correct use and interpretation.
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"." Through the introduction of the statistical software R, students will learn to apply the appropriate quantitative tools on various real-data scenarios and an adequate representation of the results. As part of their final exam, they will also be able to design and develop an "empirical exercise" on their own.
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
SECS-S/01 - STATISTICS - University credits: 6
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 80 hours
Professor:
Leorato Samantha
Professor(s)
Reception:
Next office hours: Tuesday 23-07-24 from 11 to 12:30. Friday 26th from 10 to 11:30 online only. There will be no office hours in August
Room 32 third floor