Applied Statistics

Code: MAT13640L

6 ECTS

Duration: 15 weeks/156 hours

Scientific Area: Mathematics

Teaching languages: Portuguese

Languages of tutoring support: Portuguese

Regime de Frequência: Presencial

Sustainable Development Goals

3. Saúde de qualidade - Assegurar uma vida saudável e promover o bem-estar para todos, em todas as idades.

4. Educação de qualidade - Assegurar a educação inclusiva, e equitativa e de qualidade, e promover oportunidades de aprendizagem ao longo da vida para todos.

9. Indústria, inovação e infraestruturas - Construir infraestrutura resiliente, promover a industrialização inclusiva e sustentável, e fomentar a inovação.

13. Ação climática - Tomar medidas urgentes para combater a mudança climática e seus impactos (*).

14. Proteger a vida marinha - Conservação e uso sustentável dos oceanos, dos mares, e dos recursos marinhos para o desenvolvimento sustentável.

15. Proteger a vida terrestre - Proteger, recuperar e promover o uso sustentável dos ecossistemas terrestres, gerir de forma sustentável as florestas, combater a desertificação, deter e reverter a degradação da Terra e deter a perda da biodiversidade.

Learning Goals

Outcomes:
• Knowledge of the fundamental statistical principles, concepts and tools in the analysis of various experimental designs;
• Learn to validate the assumptions of different parametric approaches and look for alternatives when they are not valid;
• Learn to analyze the association and correlation involving categorical variables;
• Knowledge of the principles of a generalized linear model (GLM) in order to identify, adjust and interpret a GLM with defined response in categories.
• Knowledge to use the concepts, methods and tools to analyze multivariate data, using description and simplification techniques and identifying patterns;

Competences:
• Ability to critically select and organize information;
• Ability to apply various statistical tools in different contexts to aid decision making;
• Ability to select the correct statistical models;
• Ability to have the capacity for abstraction, selection of statistical models and critical spirit;
• Ability to work in a team.

- Analysis of variance models: fixed effects and random effects (single and multiple factor). Multiple comparisons. Other approaches when assumptions are not verified.
- Analysis of Covariance.
- Introduction to Categorical Data Analysis. Contingency Tables. Correlation measures with at least one categorical variable. Characterization of a generalized linear model. Models with categorical response variables.
- Introduction to Principal Component Analysis.
- Introduction to Cluster Analysis.

Teaching Methods

The teaching sessions are theoretical-practical, combining the concepts with their application to concrete cases. The sessions include the resolution of exercises with the help of the statistical software of the student's choice (SPSS, R-project, Python, Excel), but predominantly in SPSS for the purposes of the outputs considered in the assessments, and using real examples whenever possible, with students actively participating in their resolution and/or group discussions. Students are also encouraged to solve practical exercises on their own in order to develop autonomy.

Assessment

The evaluation will be made in accordance with paragraph 11 of article 110 of the RAUE, considering the 2 evaluation regimes foreseen: Continuous (with 2 frequencies) or Final (per Exam). The final grade (NF), for students who obtain at least 8.0 points, in each of the frequencies, will be obtained according to the following weighting NF=0.50*F1+0.50*F2, where: F1 = Grade in the 1st frequency (50%). F2 = Grade in the 2nd frequency (50%). If the NF result is greater than or equal to 9.5, even with a grade lower than 8.0 in the 2nd frequency, the classification of the normal season will be 9 values. The use of AI tools is allowed in this course as technical, analytical and learning support, as long as students understand, validate and take full responsibility for the results produced. The misuse of sources, data or results constitutes a serious violation of academic integrity. It is unacceptable to use AI in assessments or exams without authorization. Misuse will be classified as academic fraud under Article 119 of the Academic Regulations (Code of Conduct, Fraud and Plagiarism). In case of plagiarism, the test will be invalidated and participation will be made superiorly.

Teaching Staff

Russell Gerardo Alpizar Jara [responsible]

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