Cod: 22017
Department: DCET
ECTS: 10
Scientific area: Statistics
Total working hours: 260
Total contact time: 40

At the end of this course students should know the main techniques of parametric statistical inference. They should also learn to adjust regression models, to make inference to the parameters and to know the basic principles of ANOVA and multiple comparison procedures.

Statistical Inference
Linear Regression
Analysis of Variance
Multiple Comparison Tests

At the end of this course students should have acquired skills to allow:

A comprehensive overview of the state of the art, its history and recent developments.
Feeling able to work with data sets, describing them, comparing and extrapolating results to the respective populations.
Sellecting and know how to apply and interpret statistical inference methods, parametric and nonparametric.
Recognizing the importance of Linear Regression and how to use this technique to study samples and trends.
Knowing the statistical inference applied to the regression parameters and their interpretation.
Knowing how to use and interpret the Analysis of variance with one factor.
Selecting the multiple comparison method most appropriate for each practical situation.
Getting benefit from the capabilities of software suitable for the analysis of experimental data, including the language R.

1. Parametric Statistical Inference

2. Linear Regression Models

3. Inference applied to the parameters of the regression

4. Analysis of Variance

5. Multiple Comparison Methods

·         Calapez, T., Melo P, Andrade, R., Reis, E. (2008). Estatística Aplicada, Volume 2. Edições Silabo, 6ª edição, 2019

·         Oliveira, T. (2004). Estatística Aplicada. Universidade Aberta.

·         Pruim, R. (2010). Foundations and Applications of Statistics - An introduction using R. Pure and Applied Undergraduate Texts, American Mathematical Society.

·         de Sá, J.P.M. (2007). Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Edition: 2nd ed. Berlin, Springer.


Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.

No Mestrado em Bioestatística e Biometria esta unidade curricular pode ser lecionada em regime de partilha e acompanhada em Castelhano para além do Português, quando se justifique. O docente será um professor da UNED (Universidad Nacional de Educación a Distancia, Espanha).