STATISTICS FOR ECONOMICS AND BUSINESS
Academic year and teacher
If you can't find the course description that you're looking for in the above list,
please see the following instructions >>
- Versione italiana
- Academic year
- 2022/2023
- Teacher
- STEFANO BONNINI
- Credits
- 12
- Curriculum
- Small and medium enterprises(SMEs) in international markets
- Didactic period
- Annualità Singola
- SSD
- SECS-S/01
Training objectives
- The aim of the course is to learn statistical techniques for processing data in the presence of complex datasets (multivariate problems), to support decision making processes in economics and business.
The main discussed tecniques refer to regression models (Simple Linear Regression Model - SLRM and Multiple Linear Regression Model - MLRM), factor and principal components analysis, cluster analysis (hierarchical and non hierarchical), composite indicators.
At the end of the course the student will be able:
- to know the theoretical foundations and properties of the main statistical methods of multivariate analysis
- to apply these methods to real problems
- to implement their use by means of the R software environment Prerequisites
- Basic notions related to the following topics are required:
- descriptive statistics
- probability and main probability distributions
- inferential statistics (test of hypothesis, parameters estimation, random sampling, simple linear regression) Course programme
- Module "Statistical Methods for composite indicators"
1. Basic notions of matrix algebra and of the R software environment (12 hours)
2. Factor analysis and Principal component analysis: theory, methods and applications with the use of the R software (24 hours)
3. Computatfion of composite indicators: theory, methods and applications with the use of the R software (12 hours)
Module "Statistical Methods for prediction and classification"
4. Multiple linear regression: theory, methods and applications with the use of the R software (24 hours)
5. Cluster analysis: theory, methods and applications with the use of the R software (24 hours) Didactic methods
- Lectures in the classroom.
Tutorials about R are also provided, together with theoretical lessons, to teach how to use the studied statistical methods to solve real problems and how to use the statistical software. Learning assessment procedures
- 1. Written test (multiple choice questions)
2. Practice exam (assignment and presentation)
The aim of the exam is to assess the level of achievement of the mentioned educational goals. The written test consists of 10 multiple choice questions, that is 2 questions for each of the 5 main topics: (1) matrix algebra, (2) multiple regression, (3) composite indicators, (4) cluster analysis, (5) factor analysis and principal component analysis.
The questions focus on the assessment of the knowledge of the theory, of the ability of reasoning and interpreting the results, of the use of the R commands for the application of the methods. The final score is given by the number of right answers multiplied by 3. The duration of the exam is 35 minutes.
The practice exam consists of preparing, presenting and discussing a report documenting the application of statistical methods to a real problem and a real dataset approved by the professor. This exam is evaluated with a score from 0 to 3.
The final mark is the sum of the score of the written test and the additional score of the practice exam. To pass the exam, the student must attain a minimum score of 18 in the written exam. Reference texts
- Mardia K.V., Kent J.T., Bibby J.M., «Multivariate Analysis», Academic Press, London,
Published in 2000 or later
Anderson T.W., «An introduction to Multivariate Statistical Analysis», Wiley, Published in 2003 or later
Notes of the professor and further material are available at the course Web site and/or in Google Classroom
