The course provides a practical introduction to statistics and experimental design. The aim of the first part of the course is to get the students familiar with the most useful statistical techniques for summarising, representing and finding patterns in datasets. Next, an introduction to elementary probability and distributions is provided. Then, the main methods of multivariate analysis are presented. The last part is devoted to statistical inference methods for testing hypotheses and prediction. Theoretical presentations are always motivated by practical examples and applications to conservation sciences and biotechnology. The use of the statistical package R (http://cran.r-project.org/
) is also introduced for data analyses.
Review of descriptive statistics: population and samples; types of variables; basic graphical representations and summaries for numerical variables and factors; relationship between two factors and the Chi-squared statistics; relationship between two numerical variables, correlation and regression.
Sampling and experimental design: types of samples, treatments, replications, randomization and blocking.
Probability: sample space, events and probability; independence; discrete and continuous random variables; the most important probability distributions.
Elements of multivariate analysis: principal components, linear discriminant and cluster analysis.
Inference: sample distributions; estimation of the mean and the standard deviation of a population; confidence intervals; hypotheses testing and p-values; regression and analysis of variance.