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# Probability and Statistics

 Code 8549 Year 2 Semester S1 ECTS Credits 6 Workload TP(60H) Scientific area Mathematics Entry requirements Calculus I and II Mode of delivery Face to face. Work placements Not applicable. Learning outcomes The aims of this Course Unit are: - Encourage critical skills in constructing confidence intervals, formulating hypotheses and prediction and interpreting results;- Encourage the application of probabilistic and statistical methods and techniques;-Basic - Demonstrates general culture for the Probability and Statistics: historical evolution of concepts; expertise critical sense in arguing ideas.-Scientific - Demonstrates knowledge of basic Math applied to Informatics; demonstrates basic knowledge of Probability and Statistics-Operational - Know and dominates the basic mathematical language used in Probability and Statistics;-Cross Outcomes - Understands and demonstrates general principles of ethics and morality, ability for teamwork, ability to keep records organized. Syllabus 0. Introduction and Brief Review of Descriptive Statistics1. Theory of probabilities1.1 Random experiences and happenings1.2 Conditioned probability and independence of events1.3 Total probability theorem and T. Bayes 2. Real random variables and probability distributions 2.1 Distribution Function. properties2.2 Discrete and continuous random variables2.3 Parameters of a random variable2.4 Random variables of two or more dimensions. properties 3. Theoretical models: discrete and continuous 3.1 Central Limit Theorem and its applications 3.2 De Moivre–Laplace Theorem 4 Point and interval estimation 4.1 Estimators. properties 5 Hypothesis testing 5.1 Null hypothesis versus alternative hypothesis 6. Simple linear regression model 6.1 Least squares estimators 6.2 Coefficient of determination and correlation Main Bibliography - Principal:- Guimarães, R. e Cabral, J. (1997). Estatística. McGraw-Hill.- Murteira, B., Ribeiro, C., Andrade e Silva, J. e Pimenta, C. (2002). Introdução à Estatística. McGraw-Hill.- Pedrosa, A. E Gama, S. (2004). Introdução Computacional à Probabilidade e Estatística. Fundação Calouste Gulbenkian. Lisboa.- Pestana, D. Velosa, S. (2002). Introdução à Probabilidade e à Estatística. Fundação Calouste Gulbenkian. Lisboa.-Complementary:- Mood, A., Graybill, F. and Boes, D. (1985). Introduction to the Theory of Statistics. 3rd edition. International Student Edition.- Draper, N. R. , Smith, H. (1998), Applied Regression Analysis, John Wiley and Sons, 3ª Edição. Teaching Methodologies and Assessment Criteria The measurement of knowledge and skills acquired by students during teaching-learning is done through two frequencies (classified from 0 to 20 values) with a weighting of 0.5. The final teaching-learning classification (0 to 20 points) is calculated as follows: CEA=0.5F_1+0.5F_2.Exemption from the final exam is granted when the final teaching-learning classification is equal to or greater than 9.5 values and attendance exceeds 50%.The "FREQUENCY" classification is granted when the final teaching-learning classification is greater than or equal to 6 values and less than 9.5 values. Language Portuguese. Tutorial support is available in English.

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Electrical and Computer Engineering
Last updated on: 2024-01-25

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