- Docente: Rosamarie Frieri
- Crediti formativi: 6
- SSD: SECS-S/01
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Ravenna
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Corso:
Laurea Magistrale in
International cooperation on human rights and intercultural heritage (cod. 9237)
Valido anche per Campus di Bologna
Laurea Magistrale in Scienze e gestione della natura (cod. 9257)
Conoscenze e abilità da conseguire
At the end of the course, the student learns major statistical methods to deal with ecological, economical and social data, both using univariate and multivariate approaches. The student will have the capacity to deal with the practical applications of several statisical methods to real world case and data.
Contenuti
Theory
Introduction to statistical theory. The data matrix and types of variables.
Univariate statistics. Frequency tables. Graphical methods to display data. Location measures: mean, median, mode. Measures of variability.
Bivariate statistics. Two-way tables: joint marginal and conditional distributions. Association, covariance and correlation.
Linear regression
Statistical Inference. Statistical models, population and sampling. Parametric inference: parameters estimation, confidence interval and tests. Brief introduction to tests for two sample comparisons.
Use of R
As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online
(https://elearning-sicurezza.unibo.it/?lang=en ).
Introduction to R: what is R and RStudio? R grammar and data structures. Data import and cleaning. Functions, conditionals and iteration. Basic graphs.
Statistical analysis with R: application of statistical methods explained in the first Module through R (univariate statistics, bivariate statistics, linear regression and statistical inference).
Testi/Bibliografia
G. Cicchitelli, P. D'Urso, M. Minozzo (2021) "Statistics - Principles and Methods", first edition. Pearson Italia.
Metodi didattici
Modalità di verifica e valutazione dell'apprendimento
Final examination consists of a written test and a practical project. The written test aims at verifying the general theory behind the studied statistical methods, application of the methods learned in the course to data and interpretation of results. The practical project aims at verifying R knowledge and it will deal with a real ecological/environmental problem that might be solved using R-software. The practical project will be discussed through an oral presentation.
Strumenti a supporto della didattica
Lecture notes, slides, R scripts and additional material posted on virtuale.
Orario di ricevimento
Consulta il sito web di Rosamarie Frieri