Learning objectives: 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.
Knowledge of descriptive and inferential statistical methods; linear algebra; calculus.
I Module (24h): Introduction to data analysis. A short excursion into Matrix Algebra.Elements of statistical descriptive and inference. Statistical survey (Data collection. Questionnaire. Sampling methods). Measuring scales. Data matrix
II Module (24h):Principal component factor analysis. Hierarchical, non-hierarchical, two-step clustering. Multivariate regression model: assumptions, parameter estimation, goodness of fit, interpretation. Logistic regression. Multivariate analysis in SPSS.
Introduction to data analysis. A short excursion into Matrix Algebra.Elements of statistical descriptive and inference. Statistical survey. Measuring scales. Data matrix Methods of data reduction and classification. Multivariate regression model. Logistic regression. Multivariate analysis in SPSS.
Lectures and applications with activestudentparticipation. The application part isdeveloped with the definition of an economicresearchobjective, the research and / or data collection, the choice and application of the most appropriate statisticalmethod, the synthesisthrough the preparation of a report. Data processing is performed with SPSS software.
The assessment is based on collective discussions in classroom of processed prepared by the students themselves and / or articles / papers related to the topics under study; and on an oral test aimed at assessing the learning abilities of the program contents.