Base Knowledge
Preliminary data analysis and graphical representation.
Teaching Methodologies
In order to achieve the objectives of the curricular unit and conference of the competencies provided to the student, the learning process is based on the study of cases, accompanied by a set of instructions and statistical/computer tools that lead the student to analyze each case and draw conclusions; in the execution of practical work of applying theoretical concepts; in the bibliographic research necessary to complement the information obtained in the classes.
Learning Results
1) Identify quantitative/qualitative data.
2) Select appropriate statistical analysis techniques to describe the data.
3) Numerically and graphically organize the results of the use of different statistical methodologies.
4) Correctly interpret numerical and graphic results.
5) Use data analysis programs (Microsoft Excel / SPSS – Statistical Package for Social Sciences).
Program
1. Descriptive statistics.
2. Elementary probability theory. Discrete and continuous one-dimensional random variables. Most common theoretical distributions: discrete and continuous.
3. Hypothesis tests for a population parameter.
4. Analysis of variance. Nonparametric tests.
5. Simple linear regression.
Curricular Unit Teachers
Cláudia Susana Pereira dos SantosGrading Methods
Continuous assessment:
Minimum presence - Regulamento Académico do 1º Ciclo de Estudos
- Test 1 (T1) - 40%
- Test 2 (T2) - 30%
- PC work (TC) - 30%
Tests and PC work are mandatory. Minimum grade per component of 8 values.
Final grade = 0,40 * T1 + 0,30 * T2 + 0,30 * TC
To obtain approval, in continuous evaluation, the student has to meet the minimum attendance and the final grade resulting from the weighted final grade equal to or greater than 9.5 values (scale 0-20).
Assessment by exam:
Written test (70%) e PC work (30%).
To obtain approval a minimum of 8 values in each assessment component and a minimum of 9.5 values (on a scale of 0-20) in the final grade is required.
In the exam for grade improvement, there is no place for the reuse of grades obtained in continuous evaluation.
Internship(s)
NAO
Bibliography
Montgomery, D. C. ( 2019). Design and Analysis of Experiments. John Wiley & Sons. 10th ed.
Maroco, J. (2021). Análise Estatística com o SPSS Statistics. Editora ReportNumber. 8ª ed.
Murteira, B., Ribeiro, C., Silva, J. & Pimenta, F. (2023). Introdução à Estatística. Escolar Editora. 4ª ed.
Reis, E., Melo, P., Andrade, R. & Calapez, T. (2021). Estatística Aplicada, vol. 1. Edições Sílabo. 7ª ed.
Reis, E., Melo, P., Andrade, R. & Calapez, T. (2019). Estatística Aplicada, vol. 2. Edições Sílabo. 6ª ed.
Pedrosa, A. C. & Gama, S. M. (2016). Introdução Computacional à Probabilidade e Estatística com Excel. Porto Editora. 3ª ed.
Levine, D., Stephan, D. & Szabat, K. (2016). Estatística: Teoria e Aplicações usando o Microsoft Excel em Português. LTC Editora. 7ª ed.