Teaching Methodologies
The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared considering active learning activities, to actively engage all students at various moments or throughout the entire class.
In the theoretical part of the lesson, the expository method will be frequently used to introduce concepts, fundamental results, and methods, interspersed with tasks that encourage active participation by all students. These tasks include posing questions to and by students, orally and/or on a platform, as well as proposing debates/discussions in small groups on certain exposed aspects/topics.
The practical part will be designed to comprehensively develop the listed skills. This will be achieved through commented exemplification of procedures and/or problem-solving under the guidance/tutoring of the teacher. Autonomous work or work in small groups will be encouraged. There will be a strong interaction between theory and practice, with a central focus on visualizing and dealing with actual scenarios.
Learning Results
Statistics is a science with specific techniques, with a large number of applications in the most diverse areas of knowledge and professional segments, particularly in accounting and auditing. Data-based decision-making depends on statistical knowledge that will be used from planning to analysing and interpreting data. This course will cover some inferential statistical methods for analysing data.
The following learning outcomes are therefore defined:
1. solve problems involving uncertainty scenarios that can be described in probabilistic terms;
2. identify and apply statistical inference methods such as estimation, testing and regression, suitable for solving real problems;
3. develop simple statistical or econometric studies;
4. use software to support the implementation of statistical techniques.
Program
1. Framework: statistical thinking
2. Probabilities and theoretical distributions
2.1. Fundamental concepts and theorems
2.2. Discrete and continuous random variables
2.3. Discrete and continuous theoretical distributions
3. Statistical inference
3.1. Introduction
3.2. Confidence intervals for different population parameters
3.3. Parametric hypothesis tests for different population parameters
3.4. Nonparametric tests
4. Linear regression model
4.1. Introduction
4.2. Estimation and inference
4.3. Model assessment
Internship(s)
NAO
Bibliography
Albright, S.C,. & Winston, W.L. (2019). Business Analytics: Data Analysis and Decision Making, 7th Edition. Cengage Learning.
Alwan, L.C., Craig, B.A., & McCabe, G.P. (2020). The Practice of Statistics for Business and Economics, 5th Edition. MacMilan.
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., & Cochran, J.J. (2019). Statistics for Business & Economics, 4th Edition. Cengage Learning.
Curto, J.D. (2019). Potenciar os Negócios? A Estatística Dá uma Ajuda! (Muitas Aplicações em Excel e poucas fórmulas…), 3.ª Edição. Edição do Autor.
Evans, J.R. (2020). Business Analytics, 3rd Edition. Pearson.
Murteira, B., Ribeiro, C.S., Silva, J.A., Pimenta, C., & Pimenta, F. (2023). Introdução à Estatística, 4.ª Edição. Escolar Editora.
Tintle, N., Chance, B.L., Cobb, G.W., Rossman, A.J., Roy, S., Swanson, T. & VanderStoep, J. (2020). Introduction to Statistical Investigations, 2nd Edition. Wiley.