First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
MOLECULAR BIOLOGY
Course unit
APPLIED STATISTICS
SCP8085059, 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
MOLECULAR BIOLOGY
SC2445, Degree course structure A.Y. 2018/19, A.Y. 2018/19
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Degree course track Common track
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination APPLIED STATISTICS
Website of the academic structure http://biologiamolecolare.scienze.unipd.it/2018/laurea_magistrale_biologiamolecolare
Department of reference Department of Biology
E-Learning website https://elearning.unipd.it/biologia/course/view.php?idnumber=2018-SC2445-000ZZ-2018-SCP8085059-N0
Mandatory attendance
Language of instruction English
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 ALESSANDRA ROSALBA BRAZZALE SECS-S/01

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
SCN1028731 APPLIED STATISTICS ALESSANDRA ROSALBA BRAZZALE SC1177

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/02 Statistics for Experimental and Technological Research 6.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 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 APPLIED STATISTICS 2018-2019 01/10/2018 30/11/2019 BRAZZALE ALESSANDRA ROSALBA (Presidente)
MASAROTTO GUIDO (Membro Effettivo)
VENTURA LAURA (Supplente)

Syllabus
Prerequisites: The style is informal and only minimal mathematical notation will be used. There no real prerequisites except elementary algebra. However, a previous introductory course in statistics is recommended.
Target skills and knowledge: - Ability to carry out some commonly used statistical analyses and to interpret their results.
- Ability to critically understand the main statistical methods used in the biological literature.
Examination methods: Written examination. Students should answer to questions concerning the statistical analysis of a real data set.
Assessment criteria: The evaluation will be based on the understanding of the main concepts and the skills in their application.
Course unit contents: - General ideas. From the research problem to the probabilistic models. Sampling. Observational and experimental studies. Statistical tests: hypotheses, p-value interpretation, error types, power. The problem of multiple comparisons/tests. Confidence intervals.
- Elementary methods. Inference on a proportion and comparisons of two proportions. Student's t: one sample, two samples, paired data. Large sample inference. Nonparametrics methods: Wilcoxon (one and two samples) and Kruskal-Wallis tests. Correlation coefficient.
- Advanced methods. One-way and two-way analysis of variance. Regression analysis: linear and logistic model. Exploring multivariate data: principal components and cluster analysis.
Planned learning activities and teaching methods: The course emphasizes statistical ideas rather than mathematical
formulations or computations. Methods and models are motivated and illustrated using a variety of real biological, environmental and medical examples. Many lessons will be conducted in the computer lab using the environment for statistical computing and graphics R (http://www.r-project.org).
Additional notes about suggested reading: Slides of the lectures and other materials made available on the net

- Different textbooks could be suggested during the first lessons on the basis of the prior preparation of the students.
Textbooks (and optional supplementary readings)
  • M.C. Whitlock and D. Schluter, The Analysis of Biological Data (2nd ed). --: MacMillan, 2014. Cerca nel catalogo
  • B. Shahbaba, Biostatistics with R. An Introduction to Statistics Through Biological Data. --: Springer, 2012. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Questioning
  • Story telling
  • Loading of files and pages (web pages, Moodle, ...)

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

Sustainable Development Goals (SDGs)
Good Health and Well-Being Industry, Innovation and Infrastructure Climate Action