Overall objectives:

1 - Show knowledge of experimental design and acquicition of data for statistical treatment
2 - Be able to manage and organize data in an efficient and standardized way
3 - Demonstrate knowledge of the main types of statistical analysis and show proficiency in basic exploratory and statistical analysis
4 - Be able to frame biological problems using mathematical and statistical tools
5 - Be able to create quantitative models to solve real world problems in appropriate contexts
6 - Demonstrate competency in communicating numeric and statistical results in biological sciences

Syllabus:

1 - Revision of data management using Excel and basic statistics in biological sciences
2 - Linear regression, interactions, covariance analysis, GLM models
3 - Introduction to multivariate and exploratory data analysis, distance coefficients (Euclidean, modified Euclidean, Manhattan, and others) or correlation coefficients; usage of Jaccard, simple matching or DICE coefficients
4 - Introduction to meta-analysis

Literature/Sources:

K.A. Aho , 2014 , Foundational and applied statistics for biologists using R , CRC Press
R.A. Irizarry, M.I. Love , 2017 , Data analysis for the life sciences with R , CRC Press
D. Borcard, F. Gillet, P. Legendre , 2018 , Numerical Ecology with R , Springer
F. Husson, S. Lê, J. Pagès , 2017 , Exploratory multivariate analysis by example using R , CRC Press
A.P. Beckerman, O. Petchey, D.Z. Childs , 2017 , Getting started with R: an introduction for biologists , Oxford University Press
G. Schwarzer, J.R. Carpenter, G. Rücker , 2015 , Meta-analysis with R , Springer
A. Agresti, C.A. Franklin, B. Klingenberg , 2017 , Statistics: the art and science of learning from data , Pearson Education
J.J. Faraway , 2016 , Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models , CRC Press
A.F. Zuur, E.N. Ieno, G.M. Smith , 2007 , Analysing ecological data , Springer
W.N. Venables, D.M. Smith, R Development Core Team , 2018 , An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics, version 3.4.4 (2018-03-15) , R Foundation for Statistical Computing
A. Vickers , 2010 , What is a p-value anyway? 34 stories to help you actually understand statistics , Addison-Wesley
J. Pearl, D. Mackenzie , 2018 , The book of why: the new science of cause and effect , Basic Books
B.M. Bolker , 2008 , Ecological models and data in R , Princeton University Press
S.S. Qian , 2016 , Environmental and ecological statistics with R , CRC Press
M.C. Whitlock, D. Schluter , 2015 , The analysis of biological data , Roberts and Company Publishers

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
The course is composed only of theoretical-practicals (TP) in a computer classroom. At the start of each class a theoretical introduction is given, followed by the execution by the students on their PC of examples. After most classes the students receive exercises related to the classes topic to practice at home. Most examples and exercises are based on real data collected by the teachers in their research projects. Student assessment: 2 written tests (50% each).