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Applied Statistics

Code: M4091     Acronym: M4091     Level: 400

Classification Keyword
OFICIAL Mathematics

Instance: 2020/2021 - 1S Ícone do Moodle Ícone  do Teams

Active? Yes
Responsible unit: Department of Mathematics
Course/CS Responsible: Master's degree 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 3 PE_Bioinformatics and Computational Biology 1 - 6 42 162
M:BBC 20 The study plan since 2018 1 - 6 42 162
M:DS 6 Official Study Plan since 2018_M:DS 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Ana Rita Pires Gaio

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
Ana Rita Pires Gaio 3,00

Teaching language



1. Train the student for regression analysis involving continuous or discrete responses (generalized linear models)
2. Implement statistical analyses in suitable software
3. Promote critical thinking in a data analysis process (data collection, modeling, interpretation of results, ...)

Learning outcomes and competences

At the end of the curricular unit, students are expected to:
a) acquire knowledge about the organized collection of information
b) learn techniques and statistical models commonly used in data processing
c) know how to apply and implement the models studied in R
d) know how to correctly choose the learned statistical models for concrete problems
e) acquire a critical spirit and the ability to interpret the results obtained.

Working method


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. 


1     0. Brief review of basic statistical inference techniques - confidence intervals and hypothesis tests.
1- Introduction to the programming language in R software environment.
2. Pearson and Spearman correlation.
3. Simple linear regression.
4. Multiple linear regression. Model, parameter estimation, hypothesis tests for the coefficients, confidence intervals, prediction intervals, coefficient of determination, multicollinearity, model selection methods, model comparison, diagnosis.
5. Analysis of variance: 1 and 2 factors.
6. Generalized linear models. Logistic regression and Poisson regression.

Mandatory literature

Rita Gaio; Apontamentos escritos pela professora

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
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

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.




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

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 assignment consists of a written report and an oral presentation. 

3. The grade obtained in the assignment cannot be improved.

Evaluation formula: There are two evaluation formulas depending on whether or not the students submit the assignment. 

F1: For the students that submitted the assignment:
Examination [12,15]; assignment [5,8]
a1) 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.
a2) The student will only be approved if the marks obtained in the exam and in the assignment are both higher than 6 values (out of 20). 

F2: For the students that did no submit the assignment:
In this case only the examination result counts; however, the final mark for the course will never exceed 16, even if the examination grade is higher. 

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).


1) Jury: Rita Gaio and Óscar Felgueiras.

2) The way the course will be provided is conditioned to the limitations imposed by FCUP according to the evolution of the pandemic COVID19. It is not expected a 100% face-to-face scheme.
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