Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2018/2019
Belonging course: 
Course of Master's Degree Programme on ECONOMIC, FINANCIAL AND INTERNATIONAL SCIENCES
Disciplinary sector: 
ECONOMIC STATISTICS (SECS-S/03)
Language: 
Italian
Credits: 
6
Year of study: 
1
Teachers: 
Cycle: 
Second semester
Hours of front activity: 
48

Language

Italian

Course description

The aim of the course is to provide knowledge for the measurement, processing and synthesis of economic phenomena. The course includes a theoretical part in which address issues related to data analysis with particular attention to the study of multivariate statistical techniques and an application part in which the student will acquire skills to collect, process and interpret the statistical information related to the phenomena economic.
Knowledge and understanding: Demonstrate the understanding of the process from collecting the data comes to its interpretation and the capability to associate the most appropriate statistical method in relation to the predetermined goals.
Applying knowledge and understanding: Demonstrate that you have learned how to identify a research goal or business problem and then associate the most appropriate statistical methodin relation to the set goals.
Making judgements:Demonstrate that you have developed a critical approach to the issues addressed.
Communications:Be able to express in a clear and exhaustive way the themes addressed in the teaching, choosing the most appropriate way in relation to the different stakeholders ..
Lifelong learning skills: To demonstrate learning abilities with bibliographic and other methods relating to the discipline under study.

Prerequisites

Knowledge of descriptive and inferential statistical methods; linear algebra; calculus.

Syllabus

I Module: Introduction to data analysis. Data collection. Questionnaire. Sampling methods. Measuring scales. Data matrix . Linear Algebra. Multivariate data analysis. Principal component factor analysis.
II Module:Hierarchical and non-hierarchical clustering. Multivariate regression model: assumptions, parameter estimation, goodness of fit, interpretation.
Application of the real dataset of data analysis techniques (definition of an economic research goal, the dataset search, selection and application of the statistical method, report).

I Module: Introduction to data analysis. Data collection. Questionnaire. Sampling methods. Measuring scales. Data matrix . Linear Algebra. Multivariate data analysis. Principal component factor analysis.
II Module:Hierarchical and non-hierarchical clustering. Multivariate regression model: assumptions, parameter estimation, goodness of fit, interpretation.
Application of the real dataset of data analysis techniques (definition of an economic research goal, the dataset search, selection and application of the statistical method, report).

Teaching Methods

Lectures and applications with active participation of students. During the applications it is provided the use of SPSS software for data processing by means of multivariate statistical techniques and presentation in the classroom of the related processed.

Textbooks

Zani S. e Cerioli A. (2007) Analisi dei dati e Datamining per le decisioni aziendali, Giuffré Editore.
Barbaranelli C.(2007) Analisi dei dati, LED.
Fabbris L. (1997) Statistica multivariata, Mc Graw-Hill.

Learning assessment

The assessment is based on collective discussion, classroom, of processed prepared by the students themselves and / or articles / papers related to the topics under study; and on an interview aimed at assessing the learning abilities of the program contents.

More information