The course will cover modelling and data analysis applied to conservation and management using a hands-on approach with a combination of lectures and computer lab exercises.
University of Algarve
Types of variables (properties, distribution function, graphical representation). Continues and discrete variables. Categorical variables, order variables and rates. Measurement of continuous variables. Types of association between variables: correlation and dependency. Coefficients of correlation, multiple correlation, regression and determination. Properties of the correlation and regression models. Continuous variables dependent on discrete or categorical variables (ANOVA, random blocks, repeated measures, nested ANOVA, multiday ANOVA). Interaction terms (interpretation). Continuous variables dependent on continuous variables (multiple regression). Choice of terms and order of terms in multiple regression. Continuous variables dependent on continuous and discrete or categorical variables (ANCOVA). Assumptions and limitations of ANCOVA. Fixed and random factors and mixed models. Proportions dependent of continuous variables (logistic models and survival curves) Log-linear models for frequency analysis. Assumptions and limitations of linear models. Variable transformation (linearization, homogeneity of variance, normality). The Generalized linear model. Non-linear models and non parametric model fitting. Maximum likelihood estimation. Parameter estimation through resampling techniques (Jacknife, Bootstrap and Monte Carlo). The general additive model. Guidelines for experimental design and data analysis.
Understanding of basic estimation techniques, properties of different kinds of variables and models appropriate to express relationships among them. Assumptions and requirements of linear and additive models. Use of statistical packages and interpretation of outputs.
Capacity to choose appropriate models for a given set of variables. Capacity to use a statistical analysis software package (understanding menu choices and outputs of modelling routines).
Dowdy, S., S. Wearden and D. Chilko. 2004. Statistics for Research. John Wiley & Sons, Inc., Hoboken, New Jersey, 627pp.
Zuur, A.F., E.N. Ieno and G.M. Smith. 2007. Analysing Ecological Data. Springer, Newburgh, 672pp.
Morgan, G.A., N.L. Leech, G.W. Gloec and K.C. Barrett. 2004. SPSS for introductory statistics use and interpretation. Laurence Erlbaum Associates Publishers, Mahwah, New Jersy, 211pp.
Combined lecture and computer labs - 45h