Experimental Planning and Data Analysis

Code:

RM02

Acronym:

PEAD

Keywords
Classification	Keyword
OFICIAL	Research Methodologies

Instance: 2022/2023 - 1S (of 12-09-2022 to 10-02-2023)

Active?	Yes
Responsible unit:	Population Studies
Course/CS Responsible:	Master Degree in Marine Sciences - Marine Resources

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MCMRM	16	Oficial Plan 2018	1	-	5	50	135

Teaching language

Suitable for English-speaking students

Objectives

The primary goal of the unit of Experimental Design and Data Analysis is to help students gain facility in the use of common statistical models. Students will work with models where the response variable is either quantitative or categorical and predictors (or explanatory factors) are quantitative or categorical (or both).
In this training program students should acquire both theoretical and practical knowledge in biostatistics, through a process where they have to solve problems and the teacher function as guider in their individual pathway. The exposition to small biological problems will explore both theoretical and practical aspects of biostatistics, including verbal and written communication.

Learning outcomes and competences

After completing the unit students should be able to convert data into information, gaining skills in quantitative reasoning and writing. Students should be able to choose the appropriate statistical model for a particular problem, know the conditions that are typically required when fitting various models, assess whether or not the conditions for a particular model are reasonably met for a specific dataset, have some strategies for dealing with data when the conditions for a standard model are not met, use the appropriate model to make appropriate inferences.

Working method

Presencial

Program

1. Revision: Revision of basic biostatistics methods, descriptive and inferential;
2. Analysis of Variance: One-way ANOVA, Two-way ANOVA, nested ANOVA;
3. Linear Regression: Simple Linear Regression, Multiple Regression;
4. Logistic Regression: Logistic Regression, Multiple Logistic Regression.

Mandatory literature

Baldi Brigitte; «The» practice of statistics in the life sciences. ISBN: 978-1-4641-7536-7
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. ; Applied logistic regression, 3rd edition, John Wiley & Sons., 2013
Sokal Robert R.; Biometry. ISBN: 978-0-7167-8604-7
Zar Jerrold H.; Biostatistical analysis. ISBN: 978-1-29202-404-2

Comments from the literature

Complementary Bibliography:
Cadima E.L., A.M. Caramelo, M. Afonso-Dias, P. C. Barros, M.O. Tandstad, J.I. Leiva-Moreno – Sampling methods applied to fisheries science: a manual, FAO Fisheries Technical Paper No.434, FAO, 2005.
Cannon A.R., G.W. Cobb, B.A. Hartlaub, J.M. Legler et al. - STAT2 Building models for a world of data. W.H. Freeman and Company, 2014.
Hosmer D.W. and S. Lemeshow - Applied Logistic Regression, 2nd edition, John Wiley & Sons, Inc. 2000.
Robert H. - Handbook of univariate and multivariate data analysis and interpretation with SPSS, Chapman & Hall /CRC, 2006. ISBN: 1584886021

Teaching methods and learning activities

The teaching methods include the traditional face-to-face classes and an e-learning component. In the lecture sessions, the concepts of each module are introduced and reinforced by means of the resolution of small biomedical/biological problems. These face-to-face classes are combined with a Virtual Learning Environment (VLE). The use of an e-learning platform in the unit allows the delivery of static and dynamic content to the students and also the on-line participation and evaluation. Assignments are made at group level and consist in a small biological problem with a data file and a submission deadline. In the resolution of these assignments, in a format similar to a short scientific paper, students have to report effectively both statistical and biological concepts, involved in their assignments. The examination will be carried out in computers.

Software

SPSS

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	75,00
Trabalho escrito	25,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	85,00
Frequência das aulas	50,00
Total:	135,00

Eligibility for exams

Students shoul attend at least 3/4 of the classes.

Calculation formula of final grade

Final grade: 0.75xE + 0.20xTG + 0.05*C E – Final exam (minimum 7.5); TG – Weekly assignment; C - Contributions.

Classification improvement

Final grade: 0.75xE + 0.20xTG + 0.05*C E – Final exam (minimum 7.5); TG – Weekly assignment; C - Contributions.