Statistics and Probability

Code:

EEC0011

Acronym:

PEST

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2020/2021 - 1S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Electrical and Computers Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEEC	273	Syllabus	2	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

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.

Learning outcomes and competences

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.

Working method

Presencial

Program

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.

 

Mandatory literature

Douglas C. Montgomery, George C. Runger; Applied statistics and probability for engineers. ISBN: 0-471-74589-8
Douglas C. Montgomery, George C. Runger; Estatística aplicada e probabilidade para engenheiros. ISBN: 85-216-1360-1

Complementary Bibliography

Guimarães, Rui Manuel Campos; Estatística. ISBN: 978-84-481-5589-6
Dimitri P. Bertsekas and John N. Tsitsiklis; Introduction to Probability, Athena Scientific
Jay L. Devore; Modern mathematical statistics with applications. ISBN: 978-1-4614-0390-6
Papoulis, Athanasios; Probability, random variables, and stochastic processes. ISBN: 0-07-100870-5
Meyer, Paul L.; Probabilidade. ISBN: 85-216-0294-4
Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye; Probability and Statistics for Engineers and Scientists, Pearson Education International

Teaching methods and learning activities

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.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	110,00
Frequência das aulas	52,00
Total:	162,00

Eligibility for exams

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:

1. Estudantes que obtiveram frequência em anos letivos anteriores e NÃO estão inscritos em turma prática.
2. Estudantes abrangidos pelas situações especiais previstas no Regulamento

Não há lugar à substituição da classificação em qualquer dos minitestes por classificação obtida em ano anterior.

Calculation formula of final grade

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.

Classification improvement

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.