Advanced Topics in Biostatistics

Recommended Prerequisites

Biostatistics (mainly ANOVAs and single linear regression analysis).

Teaching Methods

The course has a practical approach to the use of the different tools for data analysis. After the theoretical explanation of how each method works, students have to solve exercises in which to respond to the attached questions they have to use the different tools they have learned. In the final evaluation they will have to solve an exercise with several questions associated and where the application of different tools and the decisions taken in each analysis project must be clearly justified in order to evaluate whether students understood the modus operandi of these tools.

Learning Outcomes

This course aims to provide students with advanced knowledge indispensable to the level of treatment of biological and environmental data in ecology, through the understanding and application of different methods of univariate and multivariate tools, promoting and encouraging their scientific skills and their critical abilities so they can understand, work and find appropriate solutions to the issues related to the integrated analysis of ecological and environmental data.

Work Placement(s)

No

Syllabus

1. Data exploration in uni- and multivariate analysis: graphical data exploration, normality, outliers, transformations, collinearity. 2. Regression techniques. Single linear regression: relationship significance, ANOVA, assumptions, residuals analysis, leverage, influential points. Multiple linear regression: interactions, coplots, nominal variables. 3. Generalized linear models (Poisson and binomial): deviance, overdispersion, quasi- distributions. 4. Introduction to non-linear model fitting. 5. Introduction to techniques or multivariate analysis: Terminology used; Brief description of existing techniques 6. Ordination techniques: Principal Component and Correspondence Analysis; Non-Metric Multidimentional Scalling; Principal Coordinate Analysis; Canonical Discriminant Analysis; Similarity Analysis; ANOSIM; PERMANOVA; Redundancy Analysis; Canonical Correspondence Analysis; Selection of explanatory variables; Variance decomposition techniques; Principal response curves (PRC).

Assessment Methods

Assessment
Resolution Problems: 100.0%

Bibliography

1. Jongman, R.H.G.; Ter Braak, C..J.F. e Van Tongeren, O.F.R. (Eds.) (1995) Data analysis in community and landscape ecology. Cambridge University Press, Cambridge. 299 pp.

2. Smilauer P & Leps, J. (2014) Multivariate analysis of ecological data using Canoco5. Cambridge University Press, Cambridge. 269 pp.

3. Maroco, J. (2003) Análise estatística com utilização do SPSS (2ª ed.). Edições Sílabo, Lisboa. 508 pp.

4. Quinn, G.P. e Keough, M.J. (2002) Experimental design and data analysis for biologists. Cambridge University Press, Cambridge. 537 pp.

5. Zuur, A.F.; Ieno, E.N. & Smith, G.M. (2007) Analysing Ecological Data. Springer, NewYork, U.S.A. 685 pp

6. Zuur, A.F.; Ieno, E.N.; ïWalker, N.J.; Saveliev, A.A. & Smith, G.M. (2009) Mixed Effects Models and Extensions in Ecology with R. Springer. 549 pp.