Learning outcomes and their compatibility with the teaching method (knowledge, skills and competencies to be developed by students)
¿ Propose solutions and make decisions based on statistical modeling, data analysis, and respective interpretation;
¿ Lifelong learning of quantitative subjects;
¿ Basics of research;
¿ Use of software tools that enable statistical modeling and data analysis in professional and research environments.
Syllabus
1.Data Analysis Reviews.
2.Confidence Intervals and Parametric Hypothesis Testing.
3.Multivariate Data Analysis Models.
4.Practical Applications using Jamovi and SPSS.
Demonstration of the syllabus coherence with the curricular unit¿s learning objectives
The fundamental content required to achieve the learning objectives is taught.
Teaching and learning methodologies specific to the curricular unit articulated with the pedagogical model
The teaching methodology will be based on the following principles:
1.Presentation of theoretical/practical material using practical case studies;
2.Intensive use of video lesson technology to support students;
3.Completion of exercise worksheets related to each topic in the program content;
4.Continuous interaction with students, aiming to briefly review the main concepts taught in the previous lesson at the beginning of each class and to clarify any possible questions.
Assessment
Knowledge assessment, as a system that measures the knowledge assimilated, is individual in nature, allowing for the evaluation of the ability to develop or apply a topic or an approach to the studied reality. Continuous assessment is used along with a final test or exam (written, without an oral component).
Demonstration of the coherence of teaching and evaluation methodologies between the learning objectives of the curricular unit
The acquisition of knowledge and comprehension skills is primarily achieved through expository methods in class, as well as practical examples. The main learning methods used are individual exercises and practical assignments. The teaching methods employed, particularly to achieve higher-level competency objectives, require interactive and intense participation, as they use active learning techniques that balance concept and theory discussion with practical application.
Bibliography (Mandatory resources)
1.Araújo, P.A. Vídeo aulas de análise de dados.
2.Black, Ken. (2019) Business Statistics for Contemporary Decision Making. 10th edition.
3.Heumann,S., Schomaker, M., Shalabb (2016). Introduction to Statistics and Data Analysis
With Exercises, Solutions and Applications in R. Springer.
4.Pedrosa, A .C. E Gama, S.M. (2016) Introdução Computacional à Probabilidade e Estatística. 3º edição. Porto Editora.
5.Pestana, D. D. e Velosa, S. F. (2008). Introdução à Probabilidade e à Estatística: Volume I,3ª Edição. Fundação Calouste Gulbenkian.
