| Code |
15356
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| Year |
2
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| Semester |
S1
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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 |
Mathematical Calculus
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Mode of delivery |
Face-to-face
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Learning outcomes |
•To provide the student the basic knowledge in probability theory, random variables and the most relevant theoretical distributions.
•To familiarise the student with the most important concepts and methods in statistical inference, allowing him to apply these concepts to real situations.
After approval at this UC, the Student should be able to:
O1. Formalise correctly problems involving the result of randomised trials.
O2. Identify the probabilistic models, their properties and relation with other models.
O3. Demonstrate knowledge in the field of the statistical inference, with emphasis on parametric inference.
O4. Demonstrate strong predisposition for individual and group learning.
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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.
6 - Linear Regression
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Main Bibliography |
- Guimarães, R. C. e Sarsfield Cabral, J. A. (2010). Estatística - 2ª edição. Editora McGraw-Hill.
- Murteira, B., Ribeiro, C., Silva, J. e Pimenta, C. (2023). Introdução à Estatística - 4ª edição. McGraw-Hill, Lisboa.
- Paulino, C. e Branco, J. (2006), Exercícios de Probabilidades e Estatística, Escolar Editora.
- Sheldon M. Ross (2020), Introduction to Probability and Statistics for Engineers and Scientists. Sixth Edition. Academic Press.
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Language |
Portuguese. Tutorial support is available in English.
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