Class Statistics

The Statistics course is positioned as a fundamental area of engineering, providing an essential foundation for the analysis, interpretation, and processing of data in different scientific and professional contexts. This course aims to provide students with theoretical and practical knowledge that enables them to understand the main statistical methods used in research and decision-making. Its scope ranges from introductory concepts of sampling and descriptive statistics to statistical inference methods, hypothesis testing, analysis of variance, regression, and time series analysis. The course also emphasizes the use of computational tools, particularly the SPSS software, preparing students to analyze data in real-world applications, in more advanced courses, and in research projects.

Not applicable

General Concepts and Sampling. Fundamental concepts of Statistics. Population and sample. Probability and non-probability sampling. Types of data. Introduction to SPSS. Descriptive Statistics. Data classification and graphical representation. Frequency tables. Measures of location and dispersion. Skewness and kurtosis. Correlation and Regression. Interpretation of confidence intervals. Introduction to Probability Theory and Distributions. Events. Probability and its properties. Conditional probability. Discrete and continuous probability distributions. Statistical Inference. Point and interval estimation. Interpretation of confidence intervals. Hypothesis Testing. Normality and homoscedasticity. Parametric and non-parametric tests for one and two samples, independent or paired. Analysis of Variance. One-way ANOVA and Kruskal-Wallis test. Time Series.

In this course, active and innovative pedagogical methodologies will be adopted, centered on the student and promoting autonomous, participatory, and applied learning. A Flipped Classroom approach will be used, encouraging prior contact with theoretical concepts so that class time can be devoted to the practical resolution of exercises, case discussions, and clarification of doubts. Complementarily, Problem-Based Learning (PBL) strategies will be integrated through the analysis and resolution of real-world problems, fostering critical thinking and the application of statistical methods. Specific digital tools, such as SPSS, will also be explored, developing essential technical skills for data handling and interpretation. Whenever appropriate, collaborative activities and exercises using real data will be proposed, reinforcing students' ability to analyze and critically interpret results in academic and professional contexts.

Guimarães, R. C. e Cabral, J. A. S. (2007). Estatística, Editora McGraw-Hill. Maria Helena Pestana e João Nunes Gageiro, Análise de Dados para Ciências Sociais A complementaridade do SPSS, Edições Sílabo, Lisboa. OpenStax. (2023). Introductory statistics (2nd ed.). Rice University. link: https://openstax.org/details/books/introductory-statistics