Applied Statistics


After this unit, students should be able to:
1. Apply the fundamental principles of statistics in the design of research projects.
2. Calculate, interpret and summarize the results of the descriptive statistics and exploratory data analysis for the purpose of scientific publications.
3. Calculate and interpret confidence intervals.
4. Determine the sample size given the statistic al inference.
5. Validate the assumptions of parametric and non-parametric tests for independent and paired samples.
6. Know some generalized linear models, emphasizing the application of linear and logistic regression models, using the statistical program.
7. Know the basics concepts and applications of Bayesian statistics.
8. Calculate and interpret the measures of accuracy and performance of diagnostic tests.

General characterization





Responsible teacher

Luzia Gonçalves


Weekly - Se a UC for oferecida como opcional, o horário será disponibilizado no 2º semestre

Total - 36

Teaching language



Not applicable


• Daniel, W.W. (2004) Biostatistics: a foundation for analysis in the health sciences. 8th Edition. John Wiley and Sons.
• Dobson, A.J., Barnett, A.G. (2008) An Introduction to Generalized Linear Models. 3rd Edition. CRC Press.
• Gonçalves, L., Pascoal, C., Pires, A.M., Oliveira, MR (2012) Sample size for estimating a binomial proportion: comparison of different methods, Journal of Applied Statistics, 39 (11): 2453-2473.
• Gonçalves, L. , Subtil, A., Oliveira, M.R., Rosário, V.E., Lee P., Shaio, M.F. (2012) Bayesian latent class models in malaria diagnosis, PLoS ONE, Vol 7, Nº7, e40633. doi:10.1371/journal.pone.0040633.
• Hosmer, D., Lemeshow, S. (2000) Applied Logistic Regression. 2ª edição. John Wiley and Sons.
• Pepe, M. S. (2003) The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford Statistical Science Series, Oxford University Press.
• Sheskin, D. J. (2007) Handbook of Parametric and Nonparametric Statistical Procedures. 4th Edition, Chapman and Hall/CRC.

Teaching method

The total contact hours (36 hrs.) will be distributed b y 12 theoretical and practical sessions (24 hrs.) and 6 tutorial sessions (12 hrs.). A workload of 80 hours for individual study is estimated for this unit. In the practical sessions will be used statistical package (e.g. SPSS, EPITools and others) and other online platforms.

Evaluation method

The final assessment will be combined a written exam (60%) and an individual written work (40%). The exam includes different type of questions (e.g. multiple choice, true/false and essay questions). The written work is the result of the statistical analysis of a database, using a statistical package.

Subject matter

I. Statistical considerations in research protocols. Objectives and research questions. Type of variables and what statistical analysis will be carried out in future. Probabilistic sampling methods.
II. Descriptive statistics and exploratory data analysis for quantitative and qualitative variables.
III. Introduction to classical statistical inference: confidence intervals for mean and proportions. Different methods to construct a confidence interval for a proportion: Wilson, Agresti-Coull, Jeffreys and Clopper - Pearson. Concepts of hypothesis testing. The sample size and inferential statistics.
IV. Parametric and non - parametric tests for independent and correlated samples.
V. Linear Correlation.
VI. Introduction to generalized linear models: linear regression and logistic regression.
VII. Bayesian Statistics: concepts and applications in Health.
VIII. Diagnostic tests – Frequentist vs. Bayesian statistics.


Programs where the course is taught: