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 objectives that will be evaluated for the purpose of passing the final exam are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics; knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation, hypothesis testing and regression analysis problems.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; be able to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to carry out dependence/independence and regression analyses, also in the inferential context; know how to read statistical analyses carried out with the methodologies presented in the course.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.

TEACHING METHODS

Lezioni 75% Video, 6 ore di i-tivity e 25% di lezioni frontali. Le modalità didattiche sono coerenti con i risultati di apprendimento previsti e prevedono l’alternanza tra presentazione degli aspetti metodologici e applicazioni pratiche su dati reali. La frequenza non è obbligatoria. Su richiesta, sono previsti supporti specifici per studenti con DSA o disabilità, in accordo con le politiche di Ateneo.

SYLLABUS/CONTENT

Part I: Probability

1. Random experiments, outcomes, events.
2. The probability function and its axioms. Rules of probability.
3. Conditional probability and independence. Bivariate probabilities.
4. Discrete random variables and their properties.
5. Bernoulli, Binomial, Poisson distributions.
6. Continuous random variables and their properties.
7. Uniform and normal distributions.
8. Joint distributions.

Part II: Inference

1. Sampling and sampling distributions. 
2. Point estimation. Estimators and their properties.
3. Confidence intervals. 
4. Theory of statistical hypothesis testing.
5. Comparison of two means.

Part IV: Relationships between variables

1. Correlations and linear regression. The simple and multiple linear regression model.
2. Statistical independence and chi-square test.

RECOMMENDED READING/BIBLIOGRAPHY

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

TEACHERS AND EXAM BOARD

Exam Board

ANDREA CIACCI

STEFANO BRACCO (President Substitute)

STEFANO MASSUCCO (President Substitute)

MARTA NAI RUSCONE (President Substitute)

LESSONS

LESSONS START

Classes will start in the first week of the second semester according to the calendar of the Department of Economics.

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 Aulaweb page.

ASSESSMENT METHODS

The questions and exercises of the written test are chosen to cover, as far as possible, all the topics of the exam program. In addition to the degree of understanding and the ability to apply knowledge, the correct use of the technical language of the discipline and the ability to read and correctly interpret statistical analyses constitute evaluation parameters.