After this unit, students should be able to: 1. Understand the importance of the use of statistics in research project design. 2. Understand the importance of randomness in the sampling process. 3. Understand the processes of data collection to ensure its quality. 4. Properly utilize concepts of exploratory data analysis and descriptive statistics. 5. Apply the concepts of point and interval estimation. 6. Know how to decide between parametric and non-parametric hypotheses tests in health applications. 7. Check the assumptions required for statistical inference. 8. Apply the concepts of linear correlation and association. 9. Be able to specify the model of multiple linear regression analysis, interpret the results of the estimation and evaluate the model quality of fit. 10. Correctly use the logistic regression model and interpret the estimated coefficients in terms of odds ratios. 11. Critically analyze the results produced using SPSS.
Maria do Rosário O. Martins
Weekly - 4
Total - 40
English and Portuguese
• Callegari-Jacques, S. (2003) Bioestatística – Princípios e Aplicações. Artmed Editora SA. • Daniel, W.W. (2004) Biostatistics: a foundation for analysis in the health sciences. 8th Edition. John Wiley and Sons. • Sheskin, D. J. (2007) Handbook of Parametric and Nonparametric Statistical Procedures. 4th Edition, Chapman and Hall/CRC. • G. Cunha, M. Rosário Martins, R. Sousa, F. Ferraz Oliveira. Estatística Aplicada às Ciências e Tecnologias da Saúde, Editora LIDEL. • Douglas G. Altman. Practical Statistics for Medical Research, Chapman and Hall/CRC Texts in Statistical Science. • JM Bland and Douglas G. Altman, Statistical Notes (BMJ).
Lectures and tutorials. Lectures will be both theoretical and practical, involving the analysis of databases through the use of statistical programs such as SPSS. The discussion of articles is also promoted.
Examination and/or assignment. The exam includes multiple choice and development questions, lasting from two hours. The assignment will include the analysis of databases using statistical programs.
I. Statistical framework for research in health and development. II. Definition and classification of variables. Some caution in the collection and computerization of data. III. Exploratory data analysis and descriptive statistics. IV. Statistical Inference. 1. Parameters, statistics and sampling distributions. 2. Point estimation and confidence intervals. 3. Assumptions of parametric and non-parametric tests. 4. Calculating the sample size. 5. Comparison of populations of independent samples: • Tests of Kolmogorov - Smirnov , Shapiro - Wilk and Levene's test. • t test for two independent samples versus the Mann-Whitney - Wilcoxon test. • Analysis of variance (ANOVA) and Kruskal - Wallis test. Chi-square test for homogeneity (and independence). V. Bivariate and multivariate analysis. 1. Pearson and Spearman Linear correlation. 2. Association. Chi -square and Fisher's exact test. 3. Multiple Linear Regression. 4. Logistic Regression.
Programs where the course is taught: