Applied Statistics for Mountain Agri-Environmental Analyses
A.Y. 2026/2027
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
Opportunities and limitations of the quantitative approach in ecological research. Research planning, reverse planning, and review of the main basic statistical concepts.
Multivariate analysis of ecological data. Principles and applications of cluster analysis and Principal Component Analysis (PCA).
Analysis of relationships and differences in ecological data. Analysis of Variance (ANOVA), regression analysis, and introduction to Generalized Linear Models (GLMs).
Experimental design and data analysis. Determination of sample size.
Sampling and census methods in ecology. Introduction to sampling theory, capture-mark-recapture methods, transect sampling, and abundance indices.
PRACTICAL SESSIONS
Introduction to the R environment and the basic principles of programming and data analysis.
Data management, descriptive statistics, and graphical representation of ecological data using R.
Cluster analysis and Principal Component Analysis (PCA) using R.
Analysis of Variance (ANOVA) using R.
Regression analysis using R.
Application of Generalized Linear Models (GLMs) using R.
Opportunities and limitations of the quantitative approach in ecological research. Research planning, reverse planning, and review of the main basic statistical concepts.
Multivariate analysis of ecological data. Principles and applications of cluster analysis and Principal Component Analysis (PCA).
Analysis of relationships and differences in ecological data. Analysis of Variance (ANOVA), regression analysis, and introduction to Generalized Linear Models (GLMs).
Experimental design and data analysis. Determination of sample size.
Sampling and census methods in ecology. Introduction to sampling theory, capture-mark-recapture methods, transect sampling, and abundance indices.
PRACTICAL SESSIONS
Introduction to the R environment and the basic principles of programming and data analysis.
Data management, descriptive statistics, and graphical representation of ecological data using R.
Cluster analysis and Principal Component Analysis (PCA) using R.
Analysis of Variance (ANOVA) using R.
Regression analysis using R.
Application of Generalized Linear Models (GLMs) using R.
Prerequisites for admission
The course requires basic knowledge of both ecology and statistics. Students are expected to be familiar with the main concepts of general ecology and able to interpret them in the context of ecological data analysis.
Basic knowledge of descriptive and inferential statistics is also required. The main statistical concepts needed for the course will be reviewed and reinforced during the first part of the course.
Since practical sessions and most applied activities are conducted using R, familiarity with the R environment and with the basic principles of data management and analysis is beneficial. However, the procedures required for the course activities will be introduced and further developed during the practical sessions.
Basic knowledge of descriptive and inferential statistics is also required. The main statistical concepts needed for the course will be reviewed and reinforced during the first part of the course.
Since practical sessions and most applied activities are conducted using R, familiarity with the R environment and with the basic principles of data management and analysis is beneficial. However, the procedures required for the course activities will be introduced and further developed during the practical sessions.
Teaching methods
The course combines theoretical lectures and practical sessions designed to achieve the course learning objectives and expected learning outcomes.
Lectures introduce the main concepts of quantitative ecology, the statistical foundations required for their understanding, and the methods used for the analysis of ecological data.
Practical sessions are conducted using the R environment and focus on the application of the methods presented during the lectures. During these sessions, the instructor demonstrates procedures for managing, visualizing, and analyzing ecological data, which students replicate and further explore on their own computers, thereby developing familiarity with the R language and with the main tools used for quantitative ecological analyses.
Attendance at lectures and practical sessions is strongly recommended but not mandatory.
Lectures introduce the main concepts of quantitative ecology, the statistical foundations required for their understanding, and the methods used for the analysis of ecological data.
Practical sessions are conducted using the R environment and focus on the application of the methods presented during the lectures. During these sessions, the instructor demonstrates procedures for managing, visualizing, and analyzing ecological data, which students replicate and further explore on their own computers, thereby developing familiarity with the R language and with the main tools used for quantitative ecological analyses.
Attendance at lectures and practical sessions is strongly recommended but not mandatory.
Teaching Resources
Teaching materials
Lecture slides, R scripts used during the practical sessions, and additional teaching materials will be made available through the Ariel platform.
Suggested readings
* Eberhardt L.L. A Course in Quantitative Ecology. Freely available online: http://www.afsc.noaa.gov/nmml/library/resources/pdf/Quantitative_Ecology_Course.pdf.
* Sutherland W.J. (ed.). Ecological Census Techniques. Cambridge University Press. ISBN 978-0-511-79050-8.
* Smith T.M., Smith R.L. Elements of Ecology (6th edition). Pearson. ISBN 978-88-7192-350-5.
* Kokko H. Modelling for Field Biologists and Other Interesting People. Cambridge University Press. ISBN 978-0-511-81138-8.
* Montgomery D.C. Design and Analysis of Experiments. McGraw-Hill. ISBN 978-88-386-6179-2.
None of the listed books is required. These references are provided as optional sources for further reading on specific topics covered in the course.
Lecture slides, R scripts used during the practical sessions, and additional teaching materials will be made available through the Ariel platform.
Suggested readings
* Eberhardt L.L. A Course in Quantitative Ecology. Freely available online: http://www.afsc.noaa.gov/nmml/library/resources/pdf/Quantitative_Ecology_Course.pdf.
* Sutherland W.J. (ed.). Ecological Census Techniques. Cambridge University Press. ISBN 978-0-511-79050-8.
* Smith T.M., Smith R.L. Elements of Ecology (6th edition). Pearson. ISBN 978-88-7192-350-5.
* Kokko H. Modelling for Field Biologists and Other Interesting People. Cambridge University Press. ISBN 978-0-511-81138-8.
* Montgomery D.C. Design and Analysis of Experiments. McGraw-Hill. ISBN 978-88-386-6179-2.
None of the listed books is required. These references are provided as optional sources for further reading on specific topics covered in the course.
Assessment methods and Criteria
The assessment consists of a computer-based practical test and an oral examination, both designed to verify the achievement of the expected learning outcomes.
The practical test, with an approximate duration of 30 minutes, is intended to assess the student's ability to manage, analyze, and interpret ecological data using the R software environment. Students may be asked to import, process, visualize, and statistically analyze ecological datasets, as well as to critically interpret the results obtained.
The oral examination is aimed at assessing the understanding of the theoretical concepts covered during the course and the ability to relate quantitative methods to the ecological questions addressed. Assessment will be based on the following criteria: knowledge and understanding of course contents; ability to apply quantitative methods to the analysis of ecological data; critical thinking and interpretation of results; clarity and accuracy of presentation; appropriate use of scientific language and discipline-specific terminology.
Passing the practical test is a prerequisite for admission to the oral examination. The two examinations contribute equally to the final grade.
The final grade is expressed on a 30-point scale.
The practical test, with an approximate duration of 30 minutes, is intended to assess the student's ability to manage, analyze, and interpret ecological data using the R software environment. Students may be asked to import, process, visualize, and statistically analyze ecological datasets, as well as to critically interpret the results obtained.
The oral examination is aimed at assessing the understanding of the theoretical concepts covered during the course and the ability to relate quantitative methods to the ecological questions addressed. Assessment will be based on the following criteria: knowledge and understanding of course contents; ability to apply quantitative methods to the analysis of ecological data; critical thinking and interpretation of results; clarity and accuracy of presentation; appropriate use of scientific language and discipline-specific terminology.
Passing the practical test is a prerequisite for admission to the oral examination. The two examinations contribute equally to the final grade.
The final grade is expressed on a 30-point scale.
STAT-01/A - Statistics - University credits: 6
Exercises: 16 hours
Lessons: 40 hours
Lessons: 40 hours
Professor:
Ambrosini Roberto
Shifts:
Turno
Professor:
Ambrosini RobertoProfessor(s)
Reception:
Tuesday 10-12 am by appointment to be requested via email a few days before
tower C, 6th floor, Via Celoria 26