Applied Statistics in Science and Engineering

Code:

M4060

Acronym:

M4060

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2017/2018 - 1S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CC	0	Study plan since 2014/2015	1	-	6	56	162
M:EAGR	19	Study plan from 2013	1	-	6	56	162
M:EG	3	Plano de Estudos do M: ENG.GEO_2013-2014	1	-	6	56	162
M:FM	0	Study plan since 2013/2014	1	-	6	56	162

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/SPSS

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 / SPSS

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. Analysis of scientific papers.

8.A

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 SPSS or the free programming language R (depending on the masters course).

Software

R Project
SPSS

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Teste	75,00
Trabalho escrito	25,00
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 written project .

   

2.   I  In order to be able not to do the final examination, students will have to mark more than 9.5 (out of 20) in their final mark (examtinations 1 and 2, and written project)

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 as a replacement of the test's mark. 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. The mark obtained in the written assignment/project cannot be improved in any evaluation periods (nor in "época normal" nor in "época de recurso"). 

Formula Evaluation: 
1st test [7,10]; 2nd test [4,7]; practical work [5,8]
From these 3 components, the one where the student had the highest score is worth the maximum of the respective interval. The worst component worths the minimum of the respective interval. The other component worhts the maximum of its interval minus 2.

Classification improvement

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 as a replacement of the test's mark. 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. The mark obtained in the written assignment/project cannot be improved in any evaluation periods (nor in "época normal" nor in "época de recurso"). 

 

f th

Observations

Formula Evaluation:
1st test [7,10]; 2nd test [4,7]; practical work [5,8]
From these 3 components, the one where the student had the highest score is worth the maximum of the respective interval. The worst component worths the minimum of the respective interval. The other component worhts the maximum of its interval minus 2.