Statistics Applied to Management II

Code: MAT02329L

6 ECTS

Duration: 15 weeks/156 hours

Scientific Area: Mathematics

Teaching languages: Portuguese

Languages of tutoring support: Portuguese

Regime de Frequência: Presencial

Presentation

It is expected that the student should be able to participate in carrying out statistical studies involving statistical treatment of data and interpretation of results, using statistical programs.

Sustainable Development Goals

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.

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

10. Reduzir as desigualdades - Reduzir as desigualdades dentro dos países e entre eles.

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

12. Produção e consumo sustentáveis - Assegurar padrões de produção e de consumo sustentáveis.

Learning Goals

This subject aims to equip the student with the necessary instruments to analyze the statistical relationship between various quantitative variables, with the subject being aimed at concrete application to cases within the scope of Management and using statistical software.
The student must be able to:
• Identify relationships and associations between variables.
• Select the most appropriate regression model and learn model adjustment and validation techniques.
• Validate the assumptions of different parametric approaches.
• Recognize and know how to apply non-parametric alternatives when the assumptions are invalid.
• Select and apply appropriate statistical methods and models to obtain conclusions that assist decision-making at the most varied levels in contexts of uncertainty.
• Learn autonomously, encouraging adaptation to new situations.
• Develop critical thinking.
• Use statistical software correctly and rationally.

1. Linear correlation analysis, univariate, linear and nonlinear regression model.
2. Multiple regression analysis. Assumptions Validation. Statistical significance of coefficients and global model assessment. Regression diagnostics. Graphical analysis of residuals.
3. Analyses of variance. One-Way analysis of variance. Validation of assumptions. Multiple comparisons. Two-way analysis of variance.
4. Non-parametric tests: Test of Chi-Square: adjustment and independence.
5. Statistical non-parametric tests for paired and independent samples.

Teaching Methods

Theoretical-practical classes are predominantly taught on the board, with the support of e-learning tools and the use of slides.
Introduction of theoretical concepts using examples of direct application in the area of Management, seeking to show the relevance of the syllabus.
Exercises aimed at the area of Management, focusing on solving current and real problems, to develop a taste and interest in the discipline and show its usefulness.
Encourage students to make classes more dynamic through individual and group work in and at home.
Motivate students to go to and continue their work.
Focus on data interpretation and analysis, whenever possible using outputs from the software used.

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 invalided and participation will be made superiorly.