| Code |
14900
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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 |
Differential and integral calculus in R
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Mode of delivery |
Face to face.
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Work placements |
Not applicable.
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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.
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Syllabus |
1. Brief introduction to R software.
2. Introduction to Probability Theory: axioms and properties of probability, conditioning and independence; discrete and continuous real random variables, distribution function, probability function and probability density function, Binomial, Geometric, Poisson, Normal distributions, moments and Central Limit Theorem.
3. Introduction to Statistical Inference: point estimation; confidence intervals; parametric and nonparametric hypothesis tests; linear regression.
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Main Bibliography |
- Gonçalves, M. E., Nogueira, M. E. e Rosa, A. C. (2020). Probabilidades e estatística para ciências e tecnologia: conceitos e exercícios resolvidos. Edições Almedina. Cota: M-7.0-00029.
- Morais, M. C. (2023). Probabilidades e Estatística. Teoria, Exemplos e Exercícios. Coleção Ensino da Ciência e da Tecnologia. IST Press.
- Montgomery, D. e Runger, G. (2011). Applied statistics and probability for engineers, 5ª Edição, John Wiley & Sons. Cota: MD-14-00531
– Ross, S. (2009). Introduction to probability and statistics for engineers and scientists. Amsterdam Elsevier. Cota: F-1.8-01370 (CD)
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Teaching Methodologies and Assessment Criteria |
Student performance is assessed through three tests (F1, F2, and F3), each graded on a 0–20 scale. The tests contribute to the final teaching-learning grade (CEA) with, respectively, the following weights 0.20, 0.35 and 0.45.
The final grade (CEA) is calculated using the formula:
CEA = 0.20 F1 + 0.35F2 + 0.45F3
Exemption from the final exam is granted when the CEA is equal to or higher than 9.5, with a minimum mark of 4 (from 0 to 20) in each test and attendance of more than 80%for firsttime enrollment in the course unit and greater than 40% for the remaining ones.
All students are admitted to the exam regardless of their CEA.
The tests will take place on the following dates:
1st Test 10/02/2025 at 6:00 p.m.
2nd Test 11/06/2025 at 6:00 p.m.
3rd Test 12/12/2025 at 6:00 p.m.
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Language |
Portuguese. Tutorial support is available in English.
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