Code: | EEC0011 | Acronym: | PEST |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Electrical and Computer Engineering |
Course/CS Responsible: | Master in Electrical and Computers Engineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
MIEEC | 273 | Syllabus | 2 | - | 6 | 56 | 162 |
This course aims to endow students with underlying knowledge of Statistics and Probability, which is indispensable to take decisions in uncertainty situations that happen in various areas of Engineering.
This course also aims to endow students with accurate communication skills when themes in the domain of Statistics and Probability are referred. Students will also develop a critical attitude in the analysis of engineering problems and they will be able to apply their knowledge in the resolution of practical problems. The adequate learning of the fundamental concepts of this course will make students able to easily learn advanced knowledge in their future career, both academic and professional.
Aquisition of a body of basic knowledge and the transmission of the very process of knowledge construction. The aim is to provide a solid preparation in mathematics skills in probabilities and statistics and prepare competences in mathematical modeling techniques fundamental for the processes of acquisition, processing and use of information.
0) Introduction to Probability and Statistics.
1) Probabilities. Classical and Axiomatic Theory. Conditional probability and independence; Bayes’ theorem.
2) Discrete Random Variables; some important distributions.
3) Continuous Random Variables; Some important distributions.
4) Random Vectors; Correlation; functions of random variables.
5) Sampling: Samples and sampling distributions.
6) Point estimate: Estimators and estimates; desirable properties of point estimates; estimation methods (method of least squares).
7) Interval estimate: Concept of confidence interval; specification of confidence intervals; sampling dimensioning.
8) Hypothesis testing: Introduction; hypothesis testing procedures; relationship between confidence intervals and hypothesis testing; dispersion and localization testing.
Theoretical-classes: presentation of the themes of the course illustrated by examples, which explain the concepts and results presented;
Theoretical-practical classes: exercises proposed and solved by the professor. Students will be encouraged to actively participate in class by suggesting solutions to the exercises and by criticizing results.
Designation | Weight (%) |
---|---|
Teste | 100,00 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Estudo autónomo | 110,00 |
Frequência das aulas | 52,00 |
Total: | 162,00 |
Para obter frequência à unidade curricular o estudante não poderá exceder o número limite de faltas às aulas teórico-práticas (25% do número de aulas previstas).
Estão isentos das condições de assiduidade:Qualquer estudante com frequência ou dispensado de frequência pode escolher obter aprovação na disciplina por minitestes ou exame de recurso. Caso um estudante não obtenha aprovação à disciplina por minitestes, pode ainda realizar o exame de recurso.
Avaliação distribuída sem exame final.
Existem 3 mini-testes (MT1, MT2, MT3).
Classificação final
CF = 0,25* MT1+ 0,25 * MT2+0,5*MT3
onde:
CF é a classificação final (de 0 a 20 valores)
MT1 a classificação no miniteste1 (de 0 e 20 valores)
MT2 a classificação no miniteste2 (de 0 e 20 valores)
MT3 a classificação no miniteste3 (de 0 e 20 valores)
No caso de realização de exame de recurso, a classificação neste exame será a classificação final.
A melhoria da classificação final será efetuada mediante a realização de exame final na época de recurso do ano letivo em que obteve aprovação ou na época de recurso do ano letivo seguinte.