Applied Statistics

Code:

M4091

Acronym:

M4091

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2019/2020 - 1S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	7	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	21	The study plan since 2018	1	-	6	42	162
M:DS	15	Official Study Plan since 2018_M:DS	1	-	6	42	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

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 the free programming language R.

Software

R Project

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

Evaluation by final examination and optional project.

1. There will be an exam in both evaluation periods.

2. The grade obtained in the project cannot be improved.

3. Evaluation formula: There are two evaluation formulas:

F1: 
Examination [12,15]; project [5,8]
From these 2 components, the one where the student had the highest score is worth the maximum of the respective interval. The worst component is worth the minimum of the respective interval.

F2: The student does not turn in the project and in this case only the examination result counts, but the final mark for the course will never exceed 16, even if the examination grade is higher. 

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

Classification improvement

Improvement of the final mark: final examination. The mark obtained in the project cannot be improved. The evaluation formula is the same (see above).