The course introduces the main statistical, mathematical, and computational tools used for the analysis of business and financial data. We start from an introduction about the data collection process and with the evaluation of data quality. Then, we focus on the main analytical tools for dealing with cross-sectional data (inference, exploratory analyses, statistical tests and regression analysis). In the follow part we introduce the most common models to analyse time series data, with a particular focus on financial markets (ARIMA, seasonal ARIMA, ARCH and GARCH models).
Throughout the whole course, we will emphasize the practical implementation of the introduced techniques using commonly used software (SAS Studio and R). In our laboratories we will work on real datasets highlighting how to interpret the obtained output.
In particular, in the Module 1 we introduce the statistical approach in the data analysis. We will also provide an overview of the main principles of survey methodology (data collection methods, sample selection, questionnaire design). We will highlight how to detect and to fix the main potential issues of collected data (data editing). A brief introduction of the statistical tests will follow. In the last part of the course, we provide an overview of two statistical techniques commonly used in cross-sectional data processing: the simple and the multiple regression estimation (and the linked ANOVA analysis and goodness of fit indicators).
The student will learn how to implement and run a data collection process (e.g., a survey project) as well as to treat issues linked to the quality of collected data. The student will also gain ability in applying the main statistical tests (e.g. the test for the mean of one or two groups) and in interpreting a statistical test output. Finally, the student will be able to set, run and interpret, in a decision making perspective, a simple or a multiple regression analysis.
No prerequisites, but a base knowledge of statistics is preferable.
For compulsory pre-requisites (Propedeuticità) see https://lt-ea.unibg.it/it/node/122
The course consists of frontal interactive lectures (stimulating students' contribution) and of laboratory sessions, where the introduced techniques are practically implemented using a software (SAS Studio and R) and fully interpreted and used.
The exam is made by two parts: one for each module (Module 1 and Module 2).
Each part awards up to 30 points. The students have to reach a positive (i.e., at least 18) score for both modules.
The final full course evaluation is computed as simple average of the two scores obtained for Module 1 and Module 2.
For both modules, the exam is written and includes an evaluation about the ability acquired by the student in working with the introduced software. After the first exam session, Module 1 exam will be oral.
There are no differences for attending and for non-attending students.
• Data collection: survey methods, sample selection, questionnaire design.
• Data quality: how to detect and treat data issues.
• Base inference: main statistical distributions and base of hypothesis testing, mean and proportion testing, testing group differences.
• Simple and multiple regression analysis: setting and running the analysis, interpreting the output (including the ANOVA table) and the model performance indexes.
• Throughout the whole course we will introduce the use of the user-friendly SAS Studio data analysis software.
The Module 1 87173-MOD 1 (3 CFU) course is part of the 87173 - 6 CFU course.
