OVERVIEW

The teaching provides the statistical knowledge and skills useful for a public decision-maker to understand and quantitatively describe the dynamics of social and economic phenomena. Half of the teaching will be laboratory activities for data analysis using appropriate statistical software tools.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to develop theoretical and applied skills for the statistical analysis of economic and social phenomena through the acquisition of the ability to apply descriptive and inferential statistical methods, construct indicators for the interpretation of socio-economic processes, and use software tools for data processing and the communication of results, with particular reference to public decision-making contexts.

AIMS AND LEARNING OUTCOMES

The course follows a didactic path on descriptive and inferential statistics that caters to the educational interests of both those who have already taken basic statistics courses and those attending a statistics course for the first time. Half of the course will be dedicated to laboratory activities for the application of the knowledge acquired during the theoretical lessons, using appropriate statistical software tools in contexts related to social and economic sciences.

• Knowledge and Understanding: Adequate knowledge is acquired on quantitative analysis methods and the interpretation of the obtained results.
• Ability to Apply Knowledge and Understanding: The ability to independently apply the learned analysis methods to new data using the statistical software presented in class is developed, and to produce simple quantitative reports that can be useful in the decision-making process of public administration.
• Judgment Autonomy: Students are able to assess the quality of the available statistical data and choose the most appropriate analysis method based on their informational needs.
• Communication Skills: Students acquire the fundamental statistical vocabulary to communicate clearly and unambiguously with both specialist and non-specialist interlocutors.
• Learning Skills: Students will be able to further explore statistical methods for social sciences by applying them with the software used in class.

PREREQUISITES

None.

TEACHING METHODS

Face-to-face lectures. All lectures will be recorded and made available for asynchronous viewing, but no mixed-mode (in-person and online) lectures are planned.

SYLLABUS/CONTENT

• Introduction to Basic Statistical Methods
• Sampling and Measurement
• Descriptive Statistics
• Probability Distributions
• Statistical Inference: Estimation
• Statistical Inference: Significance Testing
• Comparison between Two Groups
• Linear Regression and Correlation

RECOMMENDED READING/BIBLIOGRAPHY

Agresti A. (2022). Statistical Methods for the Social Sciences, Pearson (Chapters 1-9).

TEACHERS AND EXAM BOARD

Exam Board

ENRICO DI BELLA (President)

LUCA GANDULLIA

SARA PRETI (President Substitute)

PIETRO STANISLAO PARISI (Substitute)

LESSONS

LESSONS START

First semester

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written examination followed by an optional oral examination.

The written examination consists of a number of exercises to be completed in written form under open-book conditions. The oral examination (optional) consists of in-depth questions on the statistical methods discussed during the course lectures.

The open-book format allows the use of only the printed course textbook and a standard or scientific calculator. The use of personal notes, tablets, mobile phones, or any other electronic devices is not permitted.

ASSESSMENT METHODS

The written examination will assess the competencies acquired, with particular emphasis on the ability to apply the techniques covered during the course.

FURTHER INFORMATION

There are no variations in the program or assessment methods for non-attending students. However, these students are encouraged to contact their reference teacher.

Students with certification of Specific Learning Disorders (SLD), disabilities, or other special educational needs are advised to contact both the coordinator, Prof. Aristide Canepa (aristide.canepa@unige.it), and the course instructor at the beginning of the course to agree on teaching and examination methods that, while respecting the course objectives, take into account individual learning methods and provide suitable compensatory tools. For further information, please refer to the webpage: https://unige.it/disabilita-dsa/studenti-disabilità-informazioni-utili

To request compensatory tools or dispensatory measures, students with disabilities or SLD must fill in the dedicated Webform available at https://unige.it/disabilita-dsa, at least 7 working days before the exam.
Students with SEN may instead send their request by e-mail to the lecturer, copying the Department Representative, Prof. Aristide Canepa (aristide.canepa@unige.it) and the Inclusion Office (inclusione.studenti@info.unige.it).
Requests from students will be assessed by the lecturer and may be approved or rejected