Foundations of Statistical Modelling for Social and Political Sciences
A.Y. 2025/2026
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Course syllabus
During the theoretical and practical sessions will be covered the following topics
· Descriptive Statistics: Methods for summarizing and visualizing data to reveal patterns and insights.
· Probability Distributions: Theoretical foundations for understanding randomness and variability in data.
· Inferential Statistics: Techniques such as confidence intervals and hypothesis testing, which allow conclusions to be drawn from sample data.
· Modeling Approaches: Linear and generalized linear models.
· Descriptive Statistics: Methods for summarizing and visualizing data to reveal patterns and insights.
· Probability Distributions: Theoretical foundations for understanding randomness and variability in data.
· Inferential Statistics: Techniques such as confidence intervals and hypothesis testing, which allow conclusions to be drawn from sample data.
· Modeling Approaches: Linear and generalized linear models.
Prerequisites for admission
Students are expected to have a basic knowledge of calculus and a general understanding of descriptive statistics and probability theory. While prior programming experience is beneficial, it is not mandatory.
Teaching methods
The course will be conducted through interactive lectures, during which theoretical issues will be discussed and practical cases presented. The aim is to work interactively with students, encouraging their participation and organizing moments of discussion and peer interaction. In addition to the lectures, some hours of lab exercises are scheduled, where the concepts presented in class will be applied using R software.
Teaching Resources
Agresti, A., & Kateri, M. (2022). 'Foundations of Statistics for Data Scientists: With R and Python.' CRC Press.
- Instructor-prepared slides and notes available on the course platform (Ariel).
- Instructor-prepared slides and notes available on the course platform (Ariel).
Assessment methods and Criteria
There are two options for the exam:
Option A: Team work and written exam
Team Work:
Students will form groups (maximum 5 /6 people per group) to collect an analyze data relating to relevant recent problems.
Preparation of a classroom presentation is required, to be delivered in front of peers.
A detailed report, outlining the individual contributions of group members, is mandatory.
Scheduled classroom sessions will monitor and assess the progress of each group's work.
Written exam (30 minutes): The test will feature a general question related to the course material.
Students are allowed to bring a one-sided A4 formula sheet and a non-programmable calculator.
Final scoring will comprise three components:
Assessment of team work (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
Option B: Individual work and written exam
Individual work
Students are required to submit a report providing a detailed analysis of a case study of their choice. They may use data from the textbook, published articles, or public databases .
A short report, outlining the main results, is mandatory.
Preparation of a classroom presentation is required, if possible to be delivered in front of peers.
Written exam (30 minutes): The test will feature a general question related to the course material.
Students are allowed to bring a one-sided A4 formula sheet and a non-programmable calculator.
Final scoring will comprise three components:
Assessment of oral presentation (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
Option A: Team work and written exam
Team Work:
Students will form groups (maximum 5 /6 people per group) to collect an analyze data relating to relevant recent problems.
Preparation of a classroom presentation is required, to be delivered in front of peers.
A detailed report, outlining the individual contributions of group members, is mandatory.
Scheduled classroom sessions will monitor and assess the progress of each group's work.
Written exam (30 minutes): The test will feature a general question related to the course material.
Students are allowed to bring a one-sided A4 formula sheet and a non-programmable calculator.
Final scoring will comprise three components:
Assessment of team work (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
Option B: Individual work and written exam
Individual work
Students are required to submit a report providing a detailed analysis of a case study of their choice. They may use data from the textbook, published articles, or public databases .
A short report, outlining the main results, is mandatory.
Preparation of a classroom presentation is required, if possible to be delivered in front of peers.
Written exam (30 minutes): The test will feature a general question related to the course material.
Students are allowed to bring a one-sided A4 formula sheet and a non-programmable calculator.
Final scoring will comprise three components:
Assessment of oral presentation (10 points)
Evaluation of the report (10 points)
Result from the written test (10 points)
Honors will be granted to students who not only achieve the highest scores but also display active and substantial engagement in the assigned activities.
SECS-S/05 - SOCIAL STATISTICS - University credits: 9
Lessons: 60 hours
Professor:
Tarantola Claudia
Professor(s)
Reception:
Wednesday 9:30 a.m. to 12:30 p.m. (by appointment)
office n16 Via Conservatorio 7 (by appointment) or via teams (by appointment)