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ASM
This is the home page of the course ASM: Applied Statistical Modeling.
Please use the right-sidebar to navigate the pages of interest.
Instructors:
- Prof.ssa Chiara Seghieri (chiara.seghieri@santannapisa.it),
- Claudio Mazzi (claudio.mazzi@santannapisa.it)
Language: English
Duration: Nov-Dec 2026, 20h.
NOTE: The course has limited seats; potential students from the PhD in AI should send an email to Prof.ssa Seghieri (chiara.seghieri@santannapisa.it) to register for the course.
Description: The course aims to provide students with methodological and applied knowledge of statistical models for analyzing different types of response variables and data. In particular, the course will cover properties, estimation methods, and practical applications of the linear regression model, as well as generalized linear models for binary, ordinal, and count responses. Finally, the course includes a brief introduction to some of the main statistical techniques for multivariate data analysis, such as cluster analysis, principal component analysis, and factor analysis, through examples and real-world cases. The course adopts an application-oriented approach within the context of the social sciences. A preliminary knowledge of the fundamentals of Probability and Inferential Statistics is assumed.
Materials:
Statistics / David Freedman, Robert Pisani (et al.), a copy is available at the Sant’Anna
library.
Lohr, S. L. (2021). Sampling: design and analysis. Chapman and Hall/CRC.
James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: Springer, 2013.
Slides and other support materials for this course will be made available through this repository; see links in the right-sidebar.
Attendance: We expect lectures and practicum sessions to be held in person.
Evaluation Evaluation will be based on oral examination of the group project work.
Prerequisites: A working knowledge of probability and descriptive statistics.
- Introduction to Applied Statistical Modelling: General information about the course, final exam, and introduction to the theoretical statistical background
- Practical part in RStudio:
- At this link, you will find all the databases covered in the different toolkits, along with the corresponding documentation. The directory also contains other databases that you can use to practice your skills or prepare your project for the final exam.