ASM - EMbeDS-education/ComputingDataAnalysisModeling20242025 GitHub Wiki
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 ([email protected]),
- Claudio Mazzi ([email protected])
Language: English
Duration: Nov-Dec 2024, 20h.
NOTE: The course has limited seats, potential students from the PhD in AI should send an email to Prof.ssa Seghieri ([email protected]) to register to the course.
Description: The course aims at providing students with methodological and applied background on statistical models for analysing data with different types of response variables. The course has a practice-oriented approach with applications in the context of social sciences and practical examples using R software. The course focuses on linear regression, generalized linear models for binary, ordinal, and count responses, and multilevel models. The course assumes prior knowledge of the foundations of Probability and Inferential Statistics (point estimates, confidence intervals, and hypothesis testing).
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 presence.
Evaluation Evaluation will be based on oral examination from group project work.
Calendar and Syllabus:
| Date | Time | Topic (may change according to pace of classes) | Location |
|---|---|---|---|
| 14/11 | 15-17 | Introduction to the course and to linear regression | Centrale, Aula 2 |
| 19/11 | 14-17 | Linear regression: model diagnostics, multiple linear regression | Alliata, Aula 1 |
| 25/11 | 17-20 | GLM introduction, logit model | Maffi, Aula 2 |
| 29/11 | 14-17 | Probit model, ordinal logit and probit | Centrale, Aula 2 |
| 3/12 | 17-20 | Poisson regression and other GLMs | Alliata, Aula 1 |
| 9/12 | 14-17 | Random effect models | Alliata, Aula 1 |
| 11/12 | 14-17 | Recap and applications | Centrale, Aula 4 |
Prerequisites: A working knowledge of probability and descriptive statistics.