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Biostatistics
EB 2014 . 2015 - 1º semestre
Specification sheet Specific details
Learning goals
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; Syllabus
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. Prerequisites
Generic skills to reach
. Competence in analysis and synthesis;. Competence in organization and planning; . Competence for working in group; . Critical thinking; . Competence in applying theoretical knowledge in practice; . Competence to solve problems; . Competence in working in interdisciplinary teams; . Competence in autonomous learning; . Self-criticism and self-evaluation; (by decreasing order of importance) Teaching hours per semester
Assessment
Bibliography of reference
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 Teaching method
Lecturing, demonstrating, discussion and practical problem resolution.
Resources used
Sala com computadores e aplicação estatística SPSS
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