- Docente: Rosamarie Frieri
- Credits: 6
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Ravenna
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Corso:
Second cycle degree programme (LM) in
International Cooperation on Human Rights and Intercultural Heritage (cod. 9237)
Also valid for Campus of Bologna
Second cycle degree programme (LM) in Sciences and Management of Nature (cod. 9257)
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from Sep 30, 2022 to Dec 02, 2022
Learning outcomes
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.
Course contents
Theory - Module 1
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 - Module 2
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).
Readings/Bibliography
G. Cicchitelli, P. D'Urso, M. Minozzo (2021) "Statistics - Principles and Methods", first edition. Pearson Italia.
Teaching methods
Classroom lectures and Lab sessions.
Assessment methods
The final exam is composed as follows:
- Module 1: 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.
- Module 2: written test (1 hour) with multiple choice questions aimed at verifying R programming knowledge. Module 2 exam can only be taken after delivering a project in which an actual ecological/environmental issue is analysed using R and the techniques learned during the course.
The final mark will be the average of the written tests.
Teaching tools
Lecture notes, slides, R scripts and additional material posted on Virtuale. Furthermore, online material and platforms will be suggested to complete and deepen the subject.
Office hours
See the website of Rosamarie Frieri