he aim of the course is to provide students a basic knowledge of Probabilities and Statistics which are an indispensable tool for decision making under uncertainty. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study.
At the end of the unit students will have acquired skills that enable them:
-Know and understand the basic elements of the theory and the calculus of probabilities
-Describe the main probabilistic distributions of discrete and continuous variables and applies them in the description of random phenomena
-Infer about population parameters based on sample distributions
-Build-statistical models, which establish a functional relationship between variables
Ayana Maria Xavier Furtado Mateus
Weekly - 4
Total - 56
Basics of mathematical analysis, pointing out some topological notions, primitives, integrals and functions of more than one variable.
Guimarães, R.C. & Cabral, J.A.S. (2007), Estatística, McGraw-Hill.
Montgomery, D.C. & Runger, G.C. (2011), Applied Statistics and Probability for Engineers, John Wiley.
Pedrosa, A.C.& Gama, S.M.A. (2004), Introdução Computacional à Probabilidade e Estatística, Porto Editora.
Pestana, D.D. & Velosa, S.F. (2002) Introdução à Probabilidade e à Estatística, Fundação Calouste Gulbenkian, Lisboa.
Robalo, A. (1994), Estatística - Exercícios, vol. I, II, Edições Sílabo, Portugal.
Lectures and problem-solving sessions, with wide participation of students.
All students are admitted to evaluation.
The students obtain approval if the weighted average of the two tests is greater than or equal to 9.5.
Final mark = 50%T1 + 50%T2
EVALUATION BY EXAM
The evaluation by exam is valid both for grade improvement as for discipline approval. The student with a final score greater than or equal to 18.5 should carry out an oral defense of note. Otherwise, will get a final score of 18.0.
More detailed rules are available in the Portuguese version.
1. Basic notions of probability.
2. Random variables and their probability distributions.
3. Moments of random variables.
4. Some important distributions.
5. Random vectors.
6. Central limit theorem.
7. Basic notions of statistics.
8. Point and interval estimation.
9. Hypothesis testing
10. Non-parametric tests
11. Simple linear regression
12. Experimental Error
Programs where the course is taught: