| Code |
12812
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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 |
Integral and Differential Calculus.
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Mode of delivery |
Face-to-face.
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Work placements |
Not applicable.
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Learning outcomes |
To apply strategies of Statistical Inference.
At the end of this UC students should be able to:
a) identify issues of Biochemistry that can be solved with probability strategies and statistical inference;
b) build probabilistic models appropriate to the problems;
c) select and apply strategies of Statistical Inference in solving problems.
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Syllabus |
1. Basic concepts of probability
1.1 Random Experience, sample space and events
1.2 Classical definition of probability
1.3 Axiomatic definition of probability and its consequences
1.4 Conditional probability and independence of events
2. Real random variables and probability distributions
2.1 Real discrete and continuous random variables
2.2 Distribution moments
2.3 Characterization of some discrete and continuous probability distributions
2.4 Central Limit Theorem and its applications
3. Introduction to statistical inference
3.1 Point estimation and estimator properties
3.2 Confidence Intervals
3.3 Parametric and non-parametric hypothesis tests
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Main Bibliography |
Main:
Pedrosa, A., Gama, S. (2016). Introdução Computacional à Probabilidade e Estatística com Excel, Porto Editora.
Additional:
Pestana, D. e Velosa, S. (2010). Introdução à probabilidade e à estatística, 4ª Ed., Fundação Calouste Gulbenkian. Cota: I-0.0-00025
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 |
Two written tests, 10 points each, on the following dates:
October 31, 2024;
January 7, 2025.
Obtaining 9.5 or more points in total exempts the student from the final evaluation consisting of a written exam.
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Language |
Portuguese. Tutorial support is available in English.
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