Applied statistics in science and engineering

Code:

M462

Acronym:

M462

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2012/2013 - 2S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Mathematical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CC	1	PE do Mestrado em Ciência de Computadores	1	-	7,5	70	202,5
M:ENM	21	PE do Mestrado em Engenharia Matemática	1	-	7,5	70	202,5

Teaching language

Portuguese

Objectives

It is expected that at the end of the course the students will attain knowledge on:

a)     a) data collection

b)    b)  most used statistical models in the context of Science and Engineering, 

           including its application with the free software R

c)     c) the choice of the statistical model given different contexts

d)     d) the interpretation of the results obtained by the application of the learnt methods.

Learning outcomes and competences

Referred in the previous item.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Previous knowledge on random variables, probability distribution, sample statistics,

confidence intervals and hypothesis tests is required. Those are usual contents of an introductory course on Probability and Statistics for undergrduate students.

Program

1     1. Topics on data analysis with R

2.     2. Simple linear regression and correlation

3.     3. Multiple linear regression. The model, parameter estimation, hypothesis tests for the parameters, methods for selection of variables, model comparisons, diagnostics.

4.     4. Ridge and LASSO regression.

5.     5. Analysis of variance: 1 and 2 factors.

6.     6. Generalized linear models. Poisson regression, binomial (including logistic) regression, multinomial logistic regression, ordinal logistic regression.

7.     7. Survival analysis

8.     8. Analysis of scientific papers.

Mandatory literature

apontamentos escritos disponibilizados pelos professores
Julian Faraway; Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC Texts in Statistical Science, 2006. ISBN: 158488424X

Complementary Bibliography

000083800. ISBN: 1-58488-029-5
000040469. ISBN: 0-387-95475-9
000098707. ISBN: 978-0-521-86116-8
000074783. ISBN: 0-387-95187-3
000040365. ISBN: 0-387-95284-5
000102543. ISBN: 1-58488-325-1
000040221. ISBN: 0-387-98218-3
Julian Faraway; Linear Models with R, Taylor and Francis, 2009. ISBN: 1584884258

Teaching methods and learning activities

Classes will be simultaneously theoretical and practical, with several examples of application and always making use of statistical programming. The used software will be the free programming language R.

Software

R Project

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	85,00
	Teste		50,00
	Trabalho escrito		50,00
	Total:	-	100,00

Eligibility for exams

Distributed evaluation with final examination.

Calculation formula of final grade

Evaluation will be distributed with a final examination. The final mark will be computed as follows:

1.     Attainment of frequency: the students will necessarily have to develop (and maybe orally present)a project (with a weight of 50%) and to do an intermediate examination (with a weight of 50%).

        Students with less than 35% in both evaluation components loose the course frequency

       (and are therefore excluded).

2.      In order to be able not to do the final examination, students will have to mark more than 35% in each evaluation component and to obtain a sum of marks in both evaluations higher than 9.5 values (out of 20).

3.     Improvement of the final mark: students that were dispensed from the final examination but still take the examination in the first evaluation period (“época normal”) will have the mark correspondent to their examination. For the second evaluation period (“época de recurso”), improvement of the mark is as usual: the students get the best mark from what they had before the examination and from the examination mark.

Classification improvement

1.     Improvement of the final mark: students that were dispensed from the final examination but still take the examination in the first evaluation period (“época normal”) will have the mark correspondent to their examination. For the second evaluation period (“época de recurso”), improvement of the mark is as usual: the students get the best mark from what they had before the examination and from the examination mark.