Class Data Analysis II

The curricular unit Data Analysis II deepens the statistical knowledge previously acquired, in Data Analysis I, focusing on statistical inference, hypothesis testing, and introductory statistical modeling. It is centered on the practical application of statistical methods to support decision-making in the fields of Economics, Management, and Accounting. This course is essential to consolidate data analysis skills, enabling students to interpret relationships between variables and validate conclusions based on real and simulated data.

Not applicable

1. Hypothesis Testing: null and alternative hypotheses, Type I and II errors, p-value.  2. Parametric and Non-Parametric Tests: t-tests, ANOVA, Chi-square, Wilcoxon, Kruskal-Wallis, Friedman.  3. Correlation Analysis: Pearson, Spearman, Cramer’s V.  4. Linear Regression: simple and multiple, assumptions, diagnostics, and forecasting. All topics include practical application with real data in Jamovi/R.

The course adopts practical, problem-based methodologies using real data. The continuous use of statistical software (Jamovi) allows the application of concepts in real and reproducible analyses. Activities include case studies, simulations, practical reports, and critically assessed projects. The methodology encourages autonomy, analytical reasoning, and the ability to justify decisions based on statistical evidence, fostering skills relevant to professional practice.

Marôco, J. (2024). Fundamentos de estatística. ReportNumber. Newbold, P., Carlson, W. L., & Thorne, B. M. (2019). Statistics for business and economics (13th ed., Global edition). Pearson. Navarro, D. J., & Foxcroft, D. R. (2025). Learning statistics with jamovi: A tutorial for beginners in statistical analysis. Cambridge, UK: Open Book Publishers. https://doi.org/10.11647/OBP.0333