STATISTICAL METHODS FOR TOURISM

The course aims at developing the skills required in data analysis, with particular emphasis to applications for tourism to support decision making processes.

At the end of the course the student will be autonomous in the management of a simple statistical survey and in the performance of standard data analysis, in the interpretation of the results and in their communication.

No specific subject is given as a prerequisite, if not a general knowledge of the application areas.

- Data sources and indicators: the role of statistics in the switch from data to information
- The process of statistical analysis: investigation, sampling strategies, questionnaires.
- Qualitative surveys: delphi and focus group
- Data analysis, interpretation and reporting

Spiegelhalter, D. (2019). The art of statistics: Learning from data. Penguin UK.
Spiegelhalter, D. (2025). The art of uncertainty: how to navigate chance, ignorance, risk and luck. WW Norton & Company.
Lohr, S. L. (2021). Sampling: design and analysis. Chapman and Hall/CRC.
Varghese, K. A., Ranwah, B. R., Varghese, N., & Varghese, N. (2025). Research Methodology and Quantitative Techniques: A Guide for Interdisciplinary Research. Taylor & Francis.

An oral discussion on the recommended texts and the materials available on the platform. The student must demonstrate an understanding of the main concepts of the subject and the ability to argue the operational consequences of their application. Those interested may (optionally) present a critical analysis of a research study (article, report, or other) previously agreed upon with the instructor during the oral exam.

oral

28–30L: Mastery of the topics covered in lectures and textbooks; ability to organize information hierarchically; use of appropriate technical terminology.

26–27: Good knowledge of the topics covered in lectures and, to a lesser extent, in textbooks; decent ability to organize information and present it orally; familiarity with technical terminology.

24–25: Knowledge of the topics covered in lectures and textbooks is not always in-depth; oral presentation is structured but the use of technical terminology is not always accurate.

22–23: Often superficial knowledge of the topics covered in lectures and textbooks; oral presentation is unclear and lacking in technical terminology.

18–21: Partial and sometimes inadequate knowledge of the topics covered in lectures and textbooks; oral presentation is confused, with limited use of technical terminology.

Lectures will start when possible from practical examples to present also the theoretical aspects.
Given the course aims, students are encouraged to participate actively to experiment the proposed methods and techiniques.
Lab sessions using R or Excel software for the calculation of composite indicators and basic statistical models.