Quantitative Methods 2017/2018
- 6 ECTS
- Taught in Portuguese
- Both continuous and final Assessment
- relevant skillset
(i) Know and apply discrete and continuous theoretical distributions;
(ii) Know the main concepts of the theory of sampling;
(iii) Know how to use the concepts of statistical inference for:
- Build estimators and identify their properties;
- Build confidence intervals for the main population parameters;
- Implement and identify the assumptions of parametric tests to the main population parameters;
(iv) Develop critical thinking and research capacity within the statistical inference.
Basic knowledge of Descriptive Statistics, Probability Theory and Maths.
The teaching of the course is developed in theoretical / practical classes. The program's topics are explained in class using the expository method, demonstrative and interrogative, together with the completion of exercises to develop practical knowledge of the presented concepts and methods.
Body of Work
I) Typical distributions
1. Discrete distributions: Bernoulli, Binomial, Hypergeometric and Poisson;
2. Continuous distributions: Uniform, Exponential, Normal;
3. Additional distributions: T-Student, Chi-square and F-Snedecor;
4. Central Limit Theorem.
1. Probability and statistical inference;
2. General concepts
3. Sampling and Random sampling;
III) Statistical inference
1) Parameter Estimation
2) Confidence intervals
3) Hypothesis Testing
Murteira, B., Ribeiro, C., Andrade e Silva, J., Pimenta, C. Pimenta, F. (2015). Introdução à Estatística (3ª edição). Escolar Editora.
Newbold, P.; Carlson, W. and Thorne B. (2013) Statistics for Business and Economics. Prentice-Hall – Pearson Education (International Edition).
Figueiredo, F., Figueiredo, A., Ramos, A. e Teles, P. (2017) Inferência Estatística. Escolar Editora.
Maroco, J. e Bispo, R. (2005). Estatística Aplicada às Ciências Sociais e humanas. Climepsi Editores.
Murteira, B. e Antunes, M. (2012). Probabilidades e Estatística. Volume I. Escolar Editora.
1. Presentation of the syllabus, the sources of information and evaluation. Probability Theory (review).
2. Discrete distributions: Bernoulli, Binomial and Poisson.
3. Continuous distributions: Uniform, Exponential, Normal.
4. Continuous distributions
5. Central Limit Theorem and practical applications.
6. Probability and Statistical inference. Sampling and random sampling.Statistics. Estimator and estimate.
7. Properties of the estimators.
8. Moments method for estimation
9. First test and introduction of confidence intervals
10. Confidence interval for means and variance of normal populations. Study of auxiliary distributions (Student-t and Chi-square).
11. Confidence interval for proportions.
12. Hypothesis Testing: Basic concepts. Test for means of normal populations.
13. Test for means of normal populations.
14. Test for variance of normal populations.Test for proportions.
15. Practical applications of confidence interval and hypothesis testing (use of SPSS statistical software).
Demonstration of the syllabus coherence with the curricular unit's objectives
The selected program content, aim to meet consistently the learning objectives.
For the purpose (i), point (I) is directly relevant. This allows the student to know and learn the main discrete and continuous theoretical distributions necessary to perform statistical inference (decision making). Learning the basics of sampling theory, section II) of the program, allows students to achieve goal (ii). Learning the concepts of statistical inference, point III) of the program contributes to the students' achievement of a solid knowledge in this area and contributes to the objective of sensitizing them to the role of statistical inference in decision making (objective iii)). Finally, it also contributes to goal (iv), by creating practical skills through several practical exercises to develop a critical spirit.
Demonstration of the teaching methodologies coherence with the curricular unit's objectives
The union between the theoretical exposition of the subject, the participation of the students, the presentation of examples and the resolution of practical exercises on the subjects treated, allows the students to become familiar with the statistical / probabilistic methods and with the real problems (purposes (i),(ii) and (ii)).
The expository and demonstrative methods will serve to present the main concepts (purposes i), ii) and ii)). The resolution of practical exercises will be used to verify the ability of the students to apply the knowledge obtained in the classes in real practical situations of interest (objective iv)).
|relevant generic skill||improved?||assessed?|