Statistics Applied to Physical Activity

Code: MAT14972L

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.

8. Trabalho digno e crescimento económico - Promover o crescimento económico sustentado, inclusivo e sustentável, emprego pleno e produtivo, e trabalho decente para todos.

11. Cidades e comunidades sustentáveis - Tornar as cidades e os assentamentos humanos inclusivos, seguros, resilientes e sustentáveis.

17. Parcerias para a implementação dos objetivos - Fortalecer os meios de implementação e revitalizar a parceria global para o desenvolvimento sustentável.

Learning Goals

With this course, students are expected to acquire knowledge of Descriptive Statistics and Statistical Inference suitable for analyzing data related to physical activity and sports. Additionally, students should be made aware of the relevance of using these subjects in research work. Ultimately, they should be able to:
a) Describe samples using the tools learned within the scope of exploratory data analysis;
b) Solve problems in a context of uncertainty, employing the concepts and methodologies of probability theory;
c) Apply Statistical Inference in parameter estimation and in selecting the most appropriate hypothesis test for each situation;
d) Apply, interpret, and validate the assumptions of a linear regression model;
e) Perform statistical analysis, critically interpreting the results obtained using computer-based tools and digital resources.

• Exploratory Data Analysis
• Introduction to Probabilities
• Random Variables and Major Probability Distributions
• Sampling and Sampling Distributions
• Point Estimation and Confidence Intervals
• Sample Size and Effect Size
• Parametric and Non-parametric Hypothesis Testing
• Simple Analysis of Variance (One-Way ANOVA)
• Simple Linear Regression

Computer Component:
The course content will be covered using accessible and user-friendly computer software. The University of Évora has a Campus license for SPSS, but students will also be encouraged to use free software such as R-project, Jamovi, and/or JASP. Learning may also be complemented by using the following digital platforms: https://www.estimationstats.com/#/ and https://thenewstatistics.com/itns/esci/

Teaching Methods

Student-centered learning to develop both disciplinary and transversal skills through dialogue, interaction, and collaboration among peers. The discussion and resolution of proposed problems as homework and in the classroom will be enhanced. For each thematic content, concepts will be presented followed by practical examples related to physical and sports activities. Support materials will always be available on Moodle, including information about the course, lecture slides, and exercise sheets for theoretical-practical and practical-laboratory classes. In practical-laboratory classes, students will have the opportunity to use statistical software to solve problems related to the database and the applicability of the methods learned.

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, for students who obtain at least 8.5 points, in each of the frequencies, will be obtained according to the following weighting NF=0.45*F1+0.55*F2, where: F1 = Grade in the 1st frequency (45%). F2 = Grade in the 2nd frequency (55%). If the NF result is greater than or equal to 9.5, even with a grade lower than 8.5 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.