Lecturing, demonstrating, discussion and practical problem resolution.
To analyze data using appropriate statistical procedures;
To use suitable software tools to perform statistical calculations;
To plan correctly statistical studies in biomedical sciences;
To judiciously evaluate the results of studies published in the literature.
Introduction to exploratory data analysis: level of measurement, measures of location and dispersion.
Descriptive statistics and data visualization: numerical indicators and graphics. Databases: creation, labeling and debugging.
Probability: concept and algebra. Random variables and probability functions. Discrete probability distributions:
binomial and Poisson. Continuous probability distributions: normal, normal standard and t-Student. Central limit theorem.
Inferential statistics. Sample, population and sampling techniques. The estimation theory: point estimation and confidence intervals.
Statistical hypothesis and hypothesis testing. Level of significance and power of a test. P value. Parametric and non-parametric tests.
Methods of regression and correlation;
Statistical methods of supervised and unsupervised classification.
Análise Estatística, com utilização do SPSS; João Maroco, Edições Silabo;
Fundamentals of Biostatistics, Bernard Rosner, Thomson Brooks/Cole, 2006
Bioestatística, Epidemiologia e Investigação, A. Gouveia de Oliveira, Lidel
Métodos Quantitativos em Medicina, Massad, Menezes, Silveira & Ortega ed. Manole, 2004
Pattern Classification; Richard Duda, Peter Hart, David Stork; John Wiley & Sons, Inc
An Introduction of Support Vector Machines; Nello Christianini, John Shawe-Taylor; Cambridge University Press