Biostatistics

Recommended Prerequisites

Not applicable.

Teaching Methods

Lecturing, demonstrating, discussion and problem solving using suitable computer tools.

Learning Outcomes

To implement and understand regression and binary classification models in the context of supervised learning.
To interpret and critically assess the results of the models.
To analyze the model 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.

Work Placement(s)

No

Syllabus

Introduction to statistical regression and classification methods.
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.
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.

Head Lecturer(s)

Francisco José Santiago Fernandes Amado Caramelo

Assessment Methods

Assessment
Laboratory work or Field work: 30.0%
Exam: 70.0%

Bibliography

Bioestatística com SPSS: abordagem prática; Miguel Patrício, Marisa Loureiro, Francisco Caramelo; Plátano Editora, 2017

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

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.