It is intended in this course to convey concepts of probability and statistics so that students know and understand applying advanced statistical techniques for multivariate statistical description, whose purpose is to summarize and describe the most relevant aspects in a data set.
Fundamental notions will be addressed in sampling theory and discrete and continuous probabilistic models. The approach to statistical inference with reference to the hypothesis tests will be explored in more detail.

The theoretical approach will be accompanied by examples related to biology. It is also intended that the knowledge acquired in this curricular unit provides a solid basis for other curricular units in the syllabus.

1. Introduction to R: introduction to the language and data manipulation in R 2. Descriptive Statistics Analysis and graphical presentation, using R;
3. Probabilistic models: discrete and continuous;
4. Regression Analysis;

5. Hypothesis Tests:
5.1 Chi-square test;
5.2 Kolmogorov-Smirnov test and Shapiro-Wilk test; Levene test; 5.3 Parametric tests - independente samples;
(Student t-test e ANOVA test)
5.4 Nonparametric tests - independente samples;
(Wilconxon test; Mann-Whitney test; Kruskal-Wallis test)
5.5 Paired samples test;
6.Multivariate Analysis

Theoretical-practical classes where, as muc as possible, application examples will be exposed using biological data. Students will have a set of exercises (available from the Moodle ESTBarreiro / IPS) to consolidate learning, some being solved in the classroom and others performed individually with subsequent supervision of the teacher.

In class the software R will be used.

Assessment will be made through continuous assessment (test + work).

Assessment during the exam period will be done through a written exam.