STATISTICS FOR EMPIRICAL RESEARCH IN MANAGEMENT
- Academic year
- 2024/2025 Syllabus of previous years
- Official course title
- STATISTICS FOR EMPIRICAL RESEARCH IN MANAGEMENT
- Course code
- PHD169 (AF:545539 AR:312135)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Corso di Dottorato (D.M.226/2021)
- Educational sector code
- SECS-S/01
- Period
- 1st Term
- Course year
- 1
- Where
- VENEZIA
- Moodle
- Go to Moodle page
Contribution of the course to the overall degree programme goals
Expected learning outcomes
1. Knowledge and understanding
- know the terminology and basic concepts of probability and statistical inference
- understand the strengths and limitations of the statistical approaches used to analyze real phenomena.
- know the standard statistical models and some advanced methods for the analysis and the prediction and their application to Management studies.
2. Ability to apply knowledge and understanding
- understand the main aspects of the statistical analyses;
- know how to determine the best statistical models for analysis and prediction
- know how to present strategies for management studies based on the achieved results.
3. Making judgements
- be able to assess critically appraise the estimated models
- be able to critically assess under which circumstances the analyses are reliable
4. Communication
- know how to present, discuss and justify the information achieved by the analyses
- know how to report the results in written form
Pre-requirements
An introduction to statistics can be found in Ross. S.M. Introductory Statistics. 3d edition, Elsevier.
Basic understanding of the topics covered in Chapters 1-9 of the above book will be assumed throughout the course. Students may consider alternative textbooks that cover the same topics.
Contents
1. Exploring and summarizing data: Refresher of descriptive statistics
2. From data to learning and decision making: Refresher of inferential statistics
2.1 Refresher of the fundamentals of probability for statistical analysis
2.2 Statistical estimation and hypothesis testing
3. Towards model-based prediction: The simple linear regression model
3.1 Descriptive statistics for simple linear regression models: Ordinary least squares estimates (OLS)
3.2 Inference for simple linear regression models: Variability, intervals and tests
3.3 Regression-based prediction
4. Beyond simple linear regression (e.g. multiple linear regression, generalized regression models)
The practical implementation of the statistical methods presented will be showcased via adequate statistical software (e.g. R or Python)
Students are encouraged to suggest data and examples relevant to their research program
Referral texts
1. Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. 5th edition, Boston : Cengage
2. Learning statistics with Python/ Ethan Weed (https://ethanweed.github.io/pythonbook/landingpage.html )
3.Learning Statistics with R/ Danielle Navarro (https://learningstatisticswithr.com/ )
Additional readings:
- Ross. S.M. Introductory Statistics. 3d edition, Elsevier.
- Trosset, Michael W. An introduction to statistical inference and its applications with R. CRC Press
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani An Introduction to Statistical Learning: With Applications in R, 2nd edition, Springer
Additional suggested reading and materials made available on the Moodle platform
Assessment methods
- A written exam (45-60 minutes): mainly focused on the theoretical concepts presented during the course, also evaluating the capacity for critical thinking.
- A project/report (submitted via Moodle on the day of the exam): mainly focused on the practical application of the concepts presented during the course and the capacity to communicate results in a statistically formal manner.
Students must pass at least one of the two parts. This guarantees a final mark of at least 18; higher marks with depend on the grade of the other part.