| Code |
16670
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
TP(60H)
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| Scientific area |
Mathematics
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Entry requirements |
Integral calculus.
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Mode of delivery |
Theoretical and practical lessons. Face-to-face teaching
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Work placements |
Not aplicable.
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Learning outcomes |
•To provide the student the basic knowledge in probability theory, random variables and the most important theoretical distributions.
•To familiarize the student with the most important concepts and methods in statistical inference, allowing him to apply these to real situations.
After approval at this UC, the Student should be able to:
O1. Formalize correctly problems involving the result of randomized trials.
O2. Identify the probabilistic models, their properties and relation to other models.
O3. Formalize correctly problems involving the result of randomized trials.
O4. Demonstrate knowledge in the field of the statistical inference, with emphasis on parametric inference.
O5. Demonstrate strong predisposition for individual and group learning.
O6. Demonstrate the ability to interpret and analyze the results obtained using a statistical software resulting from the application of acquired knowledge.
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Syllabus |
1 - Theory of probability: conditional probability and independence. Theorem of total probability and Bayes Theorem.
2 - Random variables. Probability distributions.
3 - Theoretical Distributions: Discrete distributions. Continuous distributions.
4 - Point and Interval Estimation.
4.1 - Point estimation. Some properties of the estimators.
4.2 - Definition of confidence interval. Confidence intervals for means, variances and proportions.
5 - Hypotheses testing: Fundamental concepts. Tests for means, proportions and variances.
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Main Bibliography |
•Pedrosa, A. e Gama, S. (2004). Introdução computacional à Probabilidade e Estatística. Fundação Calouste Gulbenkian. Lisboa
•Murteira, B. (1990). Probabilidades e Estatística. Vol I e II(2ª ed.) McGraw-Hill.
•Pestana, D. D. e Velosa, S. F. (2006). Introdução à Probabilidade e à Estatística. Volume I, 2ª Edição, Fundação Calouste Gulbenkian.
•Rohatgi, V. K. (1976). An Introduction to Probability Theory and Mathematical Statistics. J. Wiley & Sons, New York.
•Ross, S. M. (1987). Introduction to Probability Theory for Engineers and Scientists. J. Wiley & Sons, New York.
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Teaching Methodologies and Assessment Criteria |
The assessment of knowledge and skills acquired by students during the teaching-learning process is carried out through two tests (graded from 0 to 20 points). The final teaching-learning grade (TLG) is calculated as follows: TLG=0.5 Test1+0.5 Test2. Exemption from the final exam is granted when the final teaching-learning grade is equal to or higher than 9.5 values. The grade ‘Attendance’ is awarded to students who cumulatively meet the following requirements: attendance in at least 50% of the classes taught; obtaining a TLG equal to or higher than 5 values and lower than 9.5 values.
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Language |
Portuguese. Tutorial support is available in English.
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