Discipline details

Course details

Recommended Prior Knowledge

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Objectives

The aim is to provide students with knowledge of Correlation, Linear Regression, Principal Component Analysis and Cluster Analysis. Students should understand and apply concepts about the contents indicated and articulate them with specific research problems in the area of Management, particularly in the area of Human Resources Management.
AO1 - Learn and carry out a correlation analysis;
AO2 - Learn and carry out a regression analysis;
AO3 - Learn and carry out principal component analysis;
AO4 - Learn and carry out a cluster analysis;
AO5 - Develop computer skills related to analysing data using appropriate software.
Teaching Methods

"The teaching/learning strategies are articulated with the objectives/outcomes expected for this CU in the following areas:
- Information on general concepts;
- Expository method and debate;
- Solving statistical problems applied to research;
- Solving practical cases;
- Computer skills;
- Application of appropriate software to solve practical cases;
- Critical evaluation of statistical results applied to research;
- Resolution of practical cases."
Internship(s)

Não
Syllabus

CP1 - Correlation Analysis
CP2 - Linear Regression
CP3 - Principal Component Analysis
CP4 - Cluster Analysis
Content Explanation

For the learning objectives defined from OA1 to OA5, and considering the previously defined programme from CP1 to CP4: CP1 addresses the topic that enables the learning objectives presented in OA1 to OA5 to be achieved; CP2 enables the learning objectives presented in OA1, OA2 and OA5 to be achieved; CP3 achieves the learning objectives presented in OA1, OA3 and OA5; CP4 achieves the learning objectives presented in OA1, OA4 and OA6.
Methodology Explanation

The teaching/learning methodologies indicated promote and motivate the student's capacity for practical application and autonomous work, which are seen as crucial aspects of this course.?On the other hand, the use of case-based learning methodology encourages student involvement and participation in the learning process.
Responsible Lecturer(s)

Sandra Maria Simões de Oliveira - 1.º Semester
Bibliography

Casella, G., & Berger, R. L. (2021). Statistical inference. Cengage Learning.
Guimarães, R. C. & Cabral, J. (2007). Estatística (2ª ed.). McGraw-Hill.
Gujarati, D.N. & Porter, D.C. (2009). Basic Econometrics. McGraw-Hill/Irwin, 5th edition.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2014), Multivariate Data Analysis, 7th Edition, Essex, UK: Pearson Education.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013).?An introduction to statistical learning. 2nd edition. New York: Springer.
Maroco, J. (2021). Análise Estatística com o SPSS (8ª Ed.). Report Number.
Mcclave, J. T., Benson & P. J, Sincich, T. (2022). Statistics for Business and Economics (14 th ed.). Pearson
Mood, A. M., Graybill, F. A. & Boes, D.C. (1974). Introduction to the Theory of Statistics (3 th ed.). McGraw-Hill International Editions.
Murteira, B., Ribeiro, C.S., Silva, J.A. & Pimenta, C. (2023). Introdução à Estatística (4ª ed.). Escolar Editora.