20149 - QUANTITATIVE METHODS FOR MANAGEMENT
Course taught in English
Go to class group/s: 19
Class 19: REBECCA GRAZIANI
This course is designed to develop students' knowledge and skills as users of quantitative methods to support management decision making. After completing the course, students are able to prepare accurate and informative data summaries for inclusion in management reports; contribute to the commissioning and the interpretation of reports of business research, including surveys, market research and program evaluations; and be able to use the main statistical techniques to support management decision making.
The course focuses on multivariate statistical techniques widely used in business analytics. Through the course students are taught how to set up the appropriate analysis, implement it through the use of a statistical software (SPSS) and give an interpretation to the obtained results. The following techniques are discussed:
- Multivariate linear regression.
- Logistic regressions.
- Factor analysis.
- Read reports and scientific articles that make use of basic and advanced statistical techniques.
- Set up and run empirical analyses, that require the use of basic and advanced statistical techniques.
- Use a statistical software (SPSS) to run multivariate statistical analyses to support management decision making.
- Contribute to the commissioning and interpretation of reports of business research, including market research and programme evaluations.
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Individual assignments
- Group assignments
- Exercises are delivered through Bboard platform for E-Learning as in-class simulation of the exams. They are multiple choice questions, with solutions provided as Feedbacks.
- Individual assignments are delivered through Bboard platform for E-Learning as takehome. Students are asked to run analyses of provided datasets with reports to be posted through Bboard platform for E-Learning. An evaluation grid is provided as well.
- Group assignments are run as marked in-class activities. Students are asked to analyse a provided dataset and write a report with the interpretation of the analyses, to be posted through Bboard platform for E-Learning . The same evaluation grid as for the individual assignments is used.
|Continuous assessment||Partial exams||General exam|
- Two partial exams or a general written exam (held in a IT room), take-homes and in-class marked activities.
- The partial exams are delivered through Bboard platform for E-Learning and graded out of 30. Students are asked to answer at multiple choice questions based on both the theory and the results of a data analysis with SPSS. The arithmetic average of the partial exams grade contributes 60% to the final mark.
- The general exam has open-ended questions to be answered based on the theory or on the results of a data analysis with SPSS. The general exam is graded out of 30 and contributes 60% to the final mark.
- Take-homes are delivered through the Bboard platform for E-Learning.Students can work alone or in group (no more than 5 students per group). If all take-homes are submitted, the student is awarded 1 point.
- In-class marked activities graded out of 31. Students are asked to run a group project consisting in an analysis of a provided dataset. A report of the analysis is submitted through Bboard platform for E-Learning at the end of the activities. The in-class activities contribute 40% to the final mark.
One general exam with open-ended questions to be answered based on the theory and on the results of a data analysis with SPSS. The general exam is graded out of 31.
- R. GRAZIANI, M. ANGELICI, Lectures notes on Multivariate Statistical Analyses with SPSS, delivered through Bboard platform for E-Learning.
- Slides of the course delivered through Bboard platform for E-Learning.
- Additional Readings: TARLING, ROGER, Statistical Modelling for Social Researchers. Principles and practice, London and New York, Routledge, 2009.