BERGAMO
Overview
Date/time interval
Syllabus
Course Objectives
The main objective of the course is to contribute in developing the student’s ability in analyzing, in understanding and in dealing with various issues linked with businesses and firms.
The course will provide students with statistical tools usable in various contexts. In particular, at the end of the course the student will be able to work with a dataset; he/she will be able to detect the best data sources as well as to evaluate the data quality. Moreover, on the one hand the student will understand the theoretical bases of some data analysis statistical methods; on the other hand he/she will try to practically apply them, using SAS Studio (SAS, from here on); this is a user-friendly platform of SAS, one of the most widespread data analysis software, worldwide (also available in the University of Bergamo’s laboratories).
More into details, the student will learn how to use a wide range of statistical methods. These will be used for cross-section analyses (preliminary explorative data analysis), for longitudinal analyses (number indexes, time series analyses) or multivariate analyses (principal components analysis, cluster analysis).
At the end of the course the student will be able to evaluate and use data (mostly business variables and datasets) and to work with them, transforming them into useful information. The student will also learn how to effectively communicate such information, translating it into operative decisions that can guide the business activities, also taking into account the competitive context.
Course Prerequisites
No mandatory or suggested prerequisite is required (the teaching approach will start from the basis, and useful statistical concepts will be re-introduced during the course).
Teaching Methods
During the course’s lectures, some of the main statistical methods will be introduced, with the support of case studies and practical examples. Within lectures, there will be a constant interaction with the students. The main scope of the theoretical introduction will be to show both how these methods work and their usefulness. At the end of the course, the student will learn how to choose and how to use the most fitting techniques. Moreover, the student will be guided in exploring and analyzing data and in the interpretation of the obtained results, so that they can become a valid support for business managers.
At the end of each theoretic section, there will be lab-sessions based on the use of a user-friendly SAS interface (SAS Studio) and of Excel. The lab sessions will be finalized to the practical application of the methods introduced during the course. In particular, the lab sessions will be mainly focused on the data analysis and on the interpretation of results, from a practical point of view. A decision-making perspective will allow students to make decisions for the business management.
In parallel to the theoretical lectures and to the lab sessions, students will have the opportunity to participate in periodic tests or other activities during the course (e.g. seminars or workshops). The evaluation of such activities, could be considered as integration or completion of the exam evaluation.
Assessment Methods
Written exam of about 90 to 120 minutes. The exam is made by a theoretical part (tests and open questions requiring short answers, other types of questions) and by a practical part (exercises or short applications). The practical part also includes an exercise to be developed using SAS Studio and/or Excel.
The theoretical and exercise part evaluation will roughly reach a maximum of 21 points, whereas the SAS Studio/Excel part is worth up to 10 points. For the first exam session an extra bonus score is available.
Other activities could be proposed during the course, in order to integrate the final score.
The exam results (on a 0 to 31 scale) will be published online and will be sent to the students by email (after this, the student is allowed to reject online his/her evaluation). The detailed scores will be published on the eLearning page of the course.
Contents
FIRST PART: from data to preliminary information.
• Statistics as support for the business management.
• Data and statistical information for business and their quality.
• First steps in working with business datasets.
• Using SAS - Working with data in order to obtain information and preliminary reports useful for decisions: variables classification, exploratory and descriptive analyses.
SECOND PART: longitudinal approaches for the analysis of business, economic and market variables (number indexes, time series analysis).
• Generic and specific statistical ratios.
• Simple and synthetic number indexes (Laspeyres, Paasche, Fisher): how to compile them, properties and interpretation.
• Using SAS/Excel: longitudinal analyses based on number indexes; decomposition and interpretation of the time changes.
• Classical analysis of time series for planning business activities: principles and objectives.
• Preliminary graphical analyses.
• Decomposition and composition models.
• Phases of the classic analysis.
• Estimating and interpreting the time series components; the moving averages.
• Using models for a medium-long term forecast.
• Models goodness of fit and of forecast.
• Using SAS/Excel - Applying decomposition and composition methods in order to study time series; using the classic analysis for forecast purposes; evaluating and comparing the forecast accuracy of forecast models.
THIRD PART: multivariate statistical analyses of business datasets.
• Examples of statistical analyses of the business balance sheet variables: choice of statistical units and variables.
• Principal component analysis (PCA) step by step.
• Estimation and interpretation of factors and practical uses.
• Benchmarking of a firm within its reference market.
• Using SAS - Reduction of variables complexity; estimate and interpretation of the principal components in order to study the business positioning within the competitive environment.
• Measuring the similarity/distance between statistical units.
• How to build a distance matrix.
• Cluster analysis introduction: how to identify homogeneous groups of firms within a market.
• Aggregative hierarchical algorithms and clustering criteria.
• Drawing and interpreting dendrograms and scree plots for practical decisions.
• Using SAS – Preliminary analysis and treatment of variables; implementing hierarchical aggregative algorithms; interpretation and practical usage of the analysis outputs; business benchmarking and study of the groups of firms.
Online Resources
More information
The course material (slides) will be published, during the course, on the web page of the course. At the end of each block of slides, some exercises and ideas are proposed, in order to help the student preparing the exam’s empirical part (exercises).
In order to support the practical ability of students in analysing data, some exercise sessions with a tutor will be organized.
Further information (e.g. news, schedule updates, full program, exams evaluations, exercises and solutions, texts of tutor activities, and so on) will be published on the eLearning page of the course (in order to get the access, please contact: daniele.toninelli@unibg.it).
Web page of the course: http://en.unibg.it/ > Study > Courses list > Dipartimento di Scienze Aziendali > “87115 – Economic Statistics”.
For further information and/or doubts, please contact the teacher: daniele.toninelli@unibg.it
Teacher’s office hour calendar: http://en.unibg.it/ > Contacts > Daniele Toninelli; link: https://www.unibg.it/ugov/person/1582