After this unit, students should be able to:
1. Identify the importance of using Statistics in the design of research projects in public health.
2. Describe the importance of randomness in the sampling process.
3. Properly use concepts of exploratory data analysis and descriptive statistics depending on the type of variable.
4. Apply the concepts of point and interval estimation.
5. Apply the main hypothesis verification tests on the distribution of variables.
6. Distinguish between parametric and non-parametric hypothesis tests.
7. Distinguish the concepts of linear, non-linear and association correlation.
8. Be able to specify the linear regression model, interpret the results of its estimation and assess the model's goodness of fit.
9. Use the logistic regression model and interpret the estimated coefficients in terms of odds ratios.
10. Critically analyze the results produced using SPSS.

General characterization





Responsible teacher

Maria do Rosário O. Martins


Weekly - 4

Total - 40

Teaching language



Not applicable


• Daniel, W.W. (2004) Biostatistics: a foundation for analysis in the health sciences. 8th Edition. John Wiley and Sons.
• 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).

Teaching method

Lectures and tutorials. Lectures will be both theoretical and practical, involving the analysis of databases through the use of statistical programs such as SPSS.

Evaluation method

Individual exam (50%) and group work (50%). The exam includes development questions, with a maximum duration of two hours. The work will be applied using statistical software.

Subject matter

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. Hypothesis tests. Steps in building a hypothesis test.
4. Kolmogorov-Smirnov and Shapiro-Wilk tests.
5. Parametric and non-parametric tests for comparison between groups: independent and paired samples:
• Test of equality of means between groups: T test for independent and paired samples.
• Test of equality of Medians between groups for independent samples: Mann-Whitney U test; Kruskal-Wallis test.
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: