First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
EVOLUTIONARY BIOLOGY
Course unit
APPLIED STATISTICS FOR EVOLUTIONARY BIOLOGY
SCP8084940, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
EVOLUTIONARY BIOLOGY (Ord. 2018)
SC1179, Degree course structure A.Y. 2018/19, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination APPLIED STATISTICS FOR EVOLUTIONARY BIOLOGY
Website of the academic structure http://biologia.scienze.unipd.it/2018/laurea_magistrale_biologiaevoluzionistica
Department of reference Department of Biology
E-Learning website https://elearning.unipd.it/biologia/course/view.php?idnumber=2018-SC1179-000ZZ-2018-SCP8084940-N0
Mandatory attendance
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge GUIDO MASAROTTO SECS-S/01
Other lecturers MAURO AGOSTINO ZORDAN BIO/18

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 2.0
Core courses SECS-S/02 Statistics for Experimental and Technological Research 2.0
Core courses BIO/18 Genetics 2.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 2.0 32 18.0 No turn
Lecture 4.0 32 68.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
1 STATISTICA APPLICATA PER LA BIOLOGIA EVOLUZIONISTICA 2018-2019 01/10/2018 30/11/2019 MASAROTTO GUIDO (Presidente)
BRAZZALE ALESSANDRA ROSALBA (Membro Effettivo)
VENTURA LAURA (Supplente)

Syllabus
Prerequisites: The approach adopted will be informal and mathematical notation will be kept to a minimum. The only real prerequisite being elementary algebra. Basic statistical knowledge is nonetheless recommended (i.e. from a course in elementary statistics).
Target skills and knowledge: - Ability to perform widely used statistical anlyses and to adequately interpret the results;
- Capacity to critically understand the main statistical methods employed in the biological literature.
Examination methods: Written exam.
Assessment criteria: Assessment will be based on the comprehension of the principle concepts and on the capacity to apply them autonomously.
Course unit contents: - Basic ideas. From the research problem to the probabilistic model. Sampling, observational and experimental studies. Satistical tests: hypotheses, interpretation of p-values, error types, power. The problem of tests/multiple comparisons. Confidence intervals.
- Elementary methods. Inference on a proportion, and comparison of two proportions. Student's single and two sample 't' for paired data. Inference in large samples. Non parametric methods: Wilcoxon (one and two samples) and Kruskall-Wallis tests. Correlation coefficient.
- Advanced methods. One and two-way analysis of variance. Regression: linear and logistic models. Exploration of multivariate data: principle components and group analysis.
Planned learning activities and teaching methods: The course emphasizes the ideas upon which the methods presented are based, and the interpretation of results and not the mathematical formulation or the calculation techniques. Numerous real-world examples, of biological and environmental relevance, will be used to justify and illustrate the various methods and models. An appreciable number of lessons will be organized in the computer laboratory using the R statistical and graphical open source package (http://www.r-project.org).
Additional notes about suggested reading: - Slides of the lessons, as well as any other useful material will be made available on line.
- Relevant reference textbooks will be suggested during the starting lessons, based on the students' previous training.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education Life on Land