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Presentation
Presentation
Provides a wide range of basic mathematical knowledge, skills and essential tools
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 5
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6643-620
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
Techniques of descriptive statistics are briefly and intensively reviewed, including regression and correlation. Basic concepts and definitions of probability theory and probability calculation are explained. The concepts of random variable, probability function, distribution are introduced and their properties explained. The main population models are described. Several examples of applying models to problem solving are explained. The interval estimation method for the mean and proportion is studied. The graphical and simplex resolution of linear programming problems are studied. Several nonlinear programming problem solving techniques are studied
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Objectives
Objectives
Calculate and interpret descriptive statistical measures and identify their properties. Understand and calculate probabilities using Laplace's definition and Kolmogorov's axiomatization. Use counting techniques: permutations, arrangements and combinations. Calculate conditioned probability and apply the Bayes' theorem. Use the concept of random variable and operate with probability functions and distributions. Know the most important discrete and continuous distributions and some of their properties. Calculate confidence intervals and apply hypothesis tests and interpret the results obtained. Use the simplex algorithm to solve linear programming problems. Calculate the free maximum / minimum of a function by the bisection and gradient method. Solve nonlinear programming problems by the Lagrange multiplier method. Evaluate the KKT conditions of an NLP problem. Solve NLP problems by the Wolfe and Lemke methods
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Teaching methodologies and assessment
Teaching methodologies and assessment
Concrete examples are presented to analyze the concepts involved. Students are encouraged to try various resolution strategies.
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References
References
Apontamentos e séries de exercícios disponibilizados na plataforma moodle.
MURTEIRA, B., ANTUNES, M., Probabilidades e Estatística, Vol. I, Escolar Editora, 2012.
MURTEIRA, B., ANTUNES, M., Probabilidades e Estatística, Vol. II, Escolar Editora, 2013.
PEDROSA, A.C., GAMA, S.M., Introdução computacional à Probabilidade e Estatística, 3a ed., Porto Editora, 2018.
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Office Hours
Office Hours
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Mobility
Mobility
No
