Class Statistical Methods

The course Statistical Methods in Accounting, Taxation and Finance is part of the scientific area of Accounting, Taxation and Finance and aims to develop advanced competencies in statistical and econometric methods for quantitative analysis at the master's level. Its scope includes the collection, organization, analysis, and interpretation of data applied to real-world problems in Accounting, Taxation, and Finance, covering statistical inference, regression analysis, time series models, qualitative response models, and multivariate techniques. The course is relevant within the study cycle as it provides a solid methodological foundation for applied research, thesis development, and evidence-based decision-making, supported by the use of open-source software (R and Jamovi), fostering up-to-date and transferable skills.

Not applicable

Syllabus (14 semanas/3h semana) Apresentação UC, avaliação, revisão estatística descritiva e R/Jamovi Probabilidade, variáveis aleatórias, distribuições discretas/contínuas Inferência estatística: estimação e intervalos de confiança Testes paramétricos (t-test, ANOVA), hipóteses, erros tipo I/II, p-valores Regressão linear simples/múltipla e diagnóstico (multicolinearidade, heteroscedasticidade) Teste 1 (semanas 1-5) Modelos logit/probit, odds ratios Séries temporais: tendência, sazonalidade, ruído Modelos ARIMA: ajuste, diagnóstico e previsão Componentes Principais: redução de dimensionalidade Clusters: métodos hierárquicos e não hierárquicos Aula prática e revisão (semanas 7-12) Teste 2 (semanas 7-12) Autoavaliação  

The teaching methodologies combine theory and practice through: theoretical-practical classes with concept exposition, applied examples, and exercises; problem-solving using real-world databases from Accounting, Taxation, and Finance; hands-on laboratory sessions with free software (R and Jamovi); individual assignments for learning consolidation; and tutorial guidance to support project development. This approach ensures active student engagement, immediate application of statistical methods, and development of autonomous, critically aware professionals prepared for the labor market without dependence on proprietary software.

Fox, J. (2015). Applied regression analysis and generalized linear models (3rd ed.). Sage Publications. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1 Marôco, J. (2024). Fundamentos de estatística [com aplicações em jamovi]. ReportNumber. R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ The jamovi project. (2024). jamovi (Version 2.4) [Computer software]. https://www.jamovi.org