Applied Statistics for Mountain Agri-Environmental Analyses
A.Y. 2024/2025
Learning objectives
To provide knowledge regarding the techniques, methods and tools for the collection, elaboration and interpretation of environmental data.
Expected learning outcomes
The student will be able to plan the collection, elaboration and interpretation of environmental data using advanced statistic methodologies and techniques.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
LECTURES
Problems and opportunities from a quantitative approach to ecological studies: planning a research program, reverse planning, basic concepts in statistics.
Application of multivariate statistical methods to ecological data: cluster analysis, principal component analysis, redundancy analysis and canonical correlation, analysis of variance, regression, and introduction to generalized linear models.
Design and analysis of experiments: sample size determination, non-parametric methods, randomized block designs.
Sampling and census methods: introduction to sampling theory, capture-mark-.recapture methods, transects, and abundance indices.
PRACTICALS
Introduction to the R statistical software and its use.
Descriptive statistics and graphs with R.
Cluster analysis, principal component analysis, redundancy analysis with R.
Analysis of variance with R.
Regression analysis with R.
Generalized linear models with R.
Problems and opportunities from a quantitative approach to ecological studies: planning a research program, reverse planning, basic concepts in statistics.
Application of multivariate statistical methods to ecological data: cluster analysis, principal component analysis, redundancy analysis and canonical correlation, analysis of variance, regression, and introduction to generalized linear models.
Design and analysis of experiments: sample size determination, non-parametric methods, randomized block designs.
Sampling and census methods: introduction to sampling theory, capture-mark-.recapture methods, transects, and abundance indices.
PRACTICALS
Introduction to the R statistical software and its use.
Descriptive statistics and graphs with R.
Cluster analysis, principal component analysis, redundancy analysis with R.
Analysis of variance with R.
Regression analysis with R.
Generalized linear models with R.
Prerequisites for admission
This is an advanced course that requires basic knowledge of both ecology and statistics. The ecological background is taken for granted (but notions can be recalled by the teacher if necessary). The statistical background is recalled during the first part of the course.
Teaching methods
This course will employ a variety of instructional methods to accomplish its objectives, including:
Lectures
Practicals where exercises are solved under the supervision of the teacher
Individual and/or Team Projects
Lectures
Practicals where exercises are solved under the supervision of the teacher
Individual and/or Team Projects
Teaching Resources
Eberhardt L.L A course in quantitative ecology (http://www.afsc.noaa.gov/nmml/library/resources/pdf/Quantitative_Ecology_Course.pdf)
Sutherland WJ (ed.), Ecological census techniques Cambridge University Press
Smith TM & Smith RL, Elementi di ecologia (6a ed.) Pearson
Kokko H Modelling for field biologists and other interesting people. Cambridge University Press
Montgomery D.C. Progettazione e analisi degli esperimenti. McGraw-Hill
Sutherland WJ (ed.), Ecological census techniques Cambridge University Press
Smith TM & Smith RL, Elementi di ecologia (6a ed.) Pearson
Kokko H Modelling for field biologists and other interesting people. Cambridge University Press
Montgomery D.C. Progettazione e analisi degli esperimenti. McGraw-Hill
Assessment methods and Criteria
Computer practice tests aimed at assessing the student's ability to analyze ecological data sets.
Oral test (question) aimed at assessing the understanding of the critical concepts illustrated in the lessons. The following will be evaluated: the level of experience and knowledge of the contents of the subject, the essential ability of reasoning, the quality of the exposure, and the use of a specialized lexicon.
Marks out of thirty.
Oral test (question) aimed at assessing the understanding of the critical concepts illustrated in the lessons. The following will be evaluated: the level of experience and knowledge of the contents of the subject, the essential ability of reasoning, the quality of the exposure, and the use of a specialized lexicon.
Marks out of thirty.
SECS-S/01 - STATISTICS - University credits: 6
Practicals: 16 hours
Lessons: 40 hours
Lessons: 40 hours