AIMS AND CONTENT

LEARNING OUTCOMES

This third contribution aims to introduce statistical methods for decision making both for public and private organisations in the environmental field. After recalling basic statistical concepts, we introduce Statistics that enables building effective models for data analysis, inference and forecasting and to support the decision-making process.

AIMS AND LEARNING OUTCOMES

The learning outcomes that will be assessed for the purpose of passing the final exam are

summarised below:

Knowledge and understanding: students will be able to describe the main tools for data

synthesis and presentation through descriptive statistics methods; explain the fundamental

concepts of probability with reference to simple random phenomena; illustrate the basic tools

of statistical inference in estimation, hypothesis testing, and regression analysis problems.

Applying knowledge and understanding: students will be able to select the appropriate

statistical techniques according to the type of data under analysis; perform simple probability

calculations in situations of uncertainty; apply the main statistical inference techniques in

exercises and application cases; carry out dependence/independence and regression

analyses, also in an inferential context; read and interpret statistical analyses carried out with

the methodologies presented in the teaching unit.

Making judgements: students will be able to interpret the results of statistical analyses in

operational terms, based on the application context of the data analysed, for decision-making

purposes.

Communication skills: students will be able to use the basic technical language of the

discipline to communicate clearly and unambiguously with specialist and non-specialist

audiences.

Learning skills: students will be able to read correctly the results of statistical analyses also in

contexts more complex than those presented in the teaching unit.

TEACHING METHODS

Lessons 75% Videos, 6 hours of e-tivity e 25% of lectures. The teaching methods are consistent with the expected learning outcomes and alternate between presentations of methodological aspects and practical applications using real data. Attendance is not compulsory. 

Please note that this course is delivered online (9 hours online videos + 6 hours in person) via the Learn platform. To request access, students must contact the Settore metodi e contenuti at edunext@aulaweb.unige.it.

Exam accommodations for students with disabilities or SLD Students with a valid disability certificate or a diagnosis of Specific Learning Disabilities (SLD) (under Italian Law 104/1992) or Special Educational Needs (SEN), who are registered with the University’s Inclusion Services, can request compensatory tools and/or dispensatory measures for exams.

Students with disabilities, with SLD or with SEN are reminded that, to request exam accommodations, they must first upload their certification to the University website at servizionline.unige.it<https://servizionline.unige.it/>, in the “Students” section. The documentation will be checked by the University’s Services for the Inclusion of Students with Disabilities and with SLD.

At the beginning of the course, students are advised to contact the lecturer to agree on exam arrangements which, while respecting the learning objectives of the course, take individual learning needs into account.

To request compensatory tools or dispensatory measures, students with disabilities or SLD must fill in the dedicated Webform available athttps://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. Elena Lagomarsino,at inclusione.economia@unige.it, and the Inclusion Office inclusione.studenti@info.unige.it.

SYLLABUS/CONTENT

Part I: Probability

Random experiments, outcomes, and events.

Probability and its axioms. Rules of probability.

Conditional probability and independence. Bivariate probabilities.

Discrete random variables and their properties.

Bernoulli, binomial, and Poisson distributions.

Continuous random variables and their properties.

Uniform and normal distributions.

Introductory elements of joint distributions.

Part II: Statistical inference

Sampling and sampling distributions.

Point estimation. Estimators and their properties.

Confidence intervals.

Basic concepts of hypothesis testing.

Tests for the comparison of means.

Part III: Relationships between variables

Correlation and linear regression. The simple and multiple linear regression model.

Statistical independence and the chi-square test.

RECOMMENDED READING/BIBLIOGRAPHY

Newbold, Carlson, Thorne, Statistica. Nona edizione. Pearson (2021).

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Please note that online lectures are scheduled to begin on September 28, 2026 (first semester).

The 6-hour lectures in-person will take place from November 16 to December 11, 2026 (first semester).

Further details about the lecture timetable will be announced at a later date.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists in a written assignment.

The examination regulations are published on the course's page.

ASSESSMENT METHODS

The questions and exercises included in the individual written assignment are selected so as

to cover, as far as possible, all the topics in the exam syllabus. The assessment verifies:

knowledge of the tools of descriptive statistics, probability, and inference; the ability to select

and apply appropriate techniques; the ability to interpret the results of statistical analyses; and

the correct use of the technical language of the discipline.

Details on exam preparation and on the expected level of depth for each topic will be

illustrated and discussed during classes.

FURTHER INFORMATION

Please contact the instructor for any further information not included in the teaching unit syllabus.