Learning outcomes of the curricular unit (knowledge, skills and competences to be developed by the students)
Objectives: acquisition of basic concepts on descriptive measures, probabilities, common statistical distributions, population, samples and sampling. Competences on sampling, description and interpretation of data, building of confidence intervals
Syllabus
Descriptive statistics. Probabilities, axioms and theorems. Linear regression, parameters and correlation coefficient. Probabilities and probability distributions, discrete and continuous random variables. Dicrete distributions: uniform, binomial, multinomial, hypergeometric, Poisson. Continuous distributions: normal, Student t. Distributions convergence. Statistical inference: confidence intervals and hypothesis testing for the mean.
Demonstration of the syllabus coherence with the curricular unit's learning objectives
The syllabus presents theoretical basis of probabilities and descriptive statistics. Practical applications and the use of statistical tools will lead to the fulfilment of the proposed objectives.
Teaching methodologies (including evaluation)
Explanatory matter in lecture sessions and application in practical classes. Evaluation through 2 tests (T1 and T2) that can provide the exam exemption when T1 and T2 individual marks are greater or equal to 8 (8/20) and (T1+T2)/2 is greater or equal to 10 (10/20). The inscription on the first test is mandatory. The final exam consists of a written test.
Demonstration of the coherence between the teaching methodologies and the learning outcomes
The presentation of the theoretical concepts will take place in lecturing classes and its application with practical examples in practical classes. The extra-work beyond class hours, with tutorial support will lead to the accomplishment of the proposed objectives.
Bibliography (Mandatory resources)
Eason G.; Coles C. W.; Gettinby G. Mathematics and Statistics for the Bio-Sciences. J Wiley&Sons, 1992. Murteira B. Análise Exploratória de Dados - Estatística Descritiva. Mc Graw-Hill de Portugal, 1999. Reis E.; Melo P.; Andrade R.; Calapez T. Estatística Aplicada. Vols 1 e 2. Sílabo, 2015, 2016. Murteira B.; Ribeiro C.S.; Silva J.A.; Pimenta C. Introdução à Estatística. Porto Editora, 2010. Pedrosa A.C.; Gama S.M.A. Introdução Computacional à Probabilidade e Estatística. Porto Editora, 2016. Guimarães R.;Sarsfield J. Estatística. Verlag Dashöfer Portugal. 2010. Taylor J. R. An Introduction to Error Analysis. Oxford University Press. 1997.
Colection of exercises and theoric chapters compiled and available in moodle.
