Probability and Statistics

Code	16147
Year	2
Semester	S1
ECTS Credits	6
Workload	TP(60H)
Scientific area	Mathematics
Entry requirements	N.A.
Learning outcomes	Obtain essential knowledge of Probability and Statistical Inference, essential to the future learning of more advanced concepts that arise in the course of academic and/or professional training. At the end of the course unit the learner is expected to be able to: 1 - identify engineering problems likely to be solved using methodologies of Probability and Statistical Inference; 2 - build probabilistic models adequate to the problems; 3 - select and apply Probability and Statistical Inference methodologies to solve problems; 4 - interpret critically and communicate rigorously the results obtained.
Syllabus	1. Introduction to Probability Theory - axiomatic and properties of probability; - conditioning and stochastic independence; - real random variables and moments; - Probabilistic models: Binomial, Geometric, Poisson, Exponential, Normal; - Central Limit Theorem and its applications. 2. Introduction to Statistical Inference: - point Estimation; - interval estimation; - hypothesis testing.
Main Bibliography	- Gonçalves, E., Nogueira, E. and Rosa, A.C. (2016). Probabilidades e Estatística para Ciências e Tecnologia. Conceitos e exercícios resolvidos. Almedina. - Montgomery, D. and Runger, G. (2011). Applied statistics and probability for engineers, 5th Edt, John Wiley & Sons. - Ross, S. (2009). Introduction to probability and statistics for engineers and scientists. Amsterdam Elsevier. - Murteira, B., Ribeiro, C., Silva, J.A. and Pimenta, C. (2010). Introdução à estatística. Escolar Editora.
Language	Portuguese. Tutorial support is available in English.