Applied Statistics in Science and Engineering

Code:

M4060

Acronym:

M4060

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2018/2019 - 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	1	Study plan since 2014/2015	1	-	6	56	162
M:EG	1	Plano de Estudos do M: ENG.GEO_2013-2014	1	-	6	56	162
M:FM	2	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. A brief review of these topics will be given. 

Program

1     0. Brief review of probability and statistics.
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. Nonparametric tests.

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 without final exam

Assessment Components

designation	Weight (%)
Teste	75,00
Trabalho escrito	25,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

Attendency is not mandatory.

Calculation formula of final grade

1. Evaluation will be distributed without a final examination. There is however  an exam in  the second evaluation period (“época de recurso”).

2. Exam in  the second evaluation period (“época de recurso”): students who have failed in the tests and project (final mark less than 9.5) can take an exam in the second evaluation period (“época de recurso”) and take one or both parts. For each part, the final score is the maximum of the marks obtained by test and exam. The mark obtained in the written assignment/project cannot be improved in any evaluation period.

3. Improvement of the final mark: students that  have succeed and attend the exam  (“época de recurso”) in order to improve their final mark, have to take both parts. The mark obtained in the written assignment/project cannot be improved in any evaluation period. The evaluation formula is the same (see below).

        4. Formula Evaluation: There are two evaluation formulas:

F1: 
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.

F2: The student does not perform the practical work/project and in this case each of the two parts (tests) is worth 50%. In this case the final mark will never exceed 16, even if the sum of the two parts is greater. 

The final classification of the student is MAX(F1,F2)

Classification improvement

Improvement of the final mark: students that  have succeed and attend the exam  (“época de recurso”) in order to improve their final mark, have to take both parts. The mark obtained in the written assignment/project cannot be improved in any evaluation period. The evaluation formula is the same (see above).

Observations

Formula Evaluation: There are two evaluation formulas:

F1: 
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.

F2: The student does not perform the practical work/project and in this case each of the two parts (tests) is worth 50%. In this case the final mark will never exceed 16, even if the sum of the two parts is greater. 

The final classification of the student is MAX(F1,F2)