- 5 ECTS
- Taught in Portuguese
- Continuous Assessment
- relevant skillset
It is intended that the student is able to:
i. Analyze and interpret a data set from the notions of Descriptive Statistics / Data Analysis.
ii.Know the counting techniques and apply combinatorial calculation in solving probability problems.
iii. Interpret, analyze and apply the theory of probability to various practical problems.
iv. Define, classify and use random variables
v. Know, identify and apply discrete theoretical distributions
The student should be able to perform statistical inferences within the course units that succeed (Quantitative Methods and Econometrics), built from the basics of descriptive statistics and probability theory.
Basic knowledge of Mathematics and Descriptive Statistics.
Presentation and discussion from case studies.
We use the lecture method to introduce the syllabus supplemented with the presentation of practical application examples (with application of SPSS).
Body of Work
1) Descriptive Statistics
1.1 Important concepts (revision): data, population/sample and variables.
1.2 Univariates distributions (revision): frequency distributions, graphical representations, descriptive measures (location, dispersion).
1.3 Bivariates distributions: correlation.
1.4 Applications (SPSS).
2) Probability Theory
2.1 Combinatorial analysis (Revision).
2.2 Introduction: randomized trials; Space results and events.
2.3 Probability Concepts. Conditional probability. Theorems. Bayes Theorem. Independence.
3) Variable Random
3.1 Random variables: definition and classification of variables.
3.2 Distribution function (properties).
3.3 Probability function of a discrete random variable. Density probability function of a continuous random variable.
3.4 Expected value. Variance and standard deviation.
Murteira, B., Ribeiro, C., Andrade e Silva, J., Pimenta, C. Pimenta, F. (2015). Introdução à Estatística (3ª edição). Escolar Editora.
Afonso, A. e Nunes, C. (2011). Estatística e Probabilidades. Aplicações e soluções em SPSS. Escolar Editora.
Newbold, P., Carlson, W. and Thorne, B. (2013). Statistics for Business and Economics. Pearson.
Figueiredo,F., Figueiredo, A., Ramos, A. e Teles,P. (2007) Estatística Descritiva e Probabilidades, Escolar Editora.
Guimarães, R. C. e Sarsfield Cabral, J. A.(2010) Estatística, Editora: Verlag Dashofer (Portugal).
Paulino, C. D. e Branco, J. (2006) Exercícios de Probabilidade e Estatística, Escolar Editora.
Pedrosa, A. e Gama, S. (2007) Introdução Computacional à Probabilidade e Estatística, Porto Editora.
Week 1: Presentation of the program content, sources of information and evaluation methods of the UC. Main concepts of Descriptive Statistics (revision).
Week 2: Frequency distributions and graphical representations (discrete and continuous variables).
Week 3: Descriptive measures of location and dispersion.
Week 4: Correlation: Pearson correlation coefficient.
Week 5: Practical applications (with application of SPSS).
Week 6: Reviews of Combinatorial analysis.
Week 7: Probability Theory: random experiences; Results space; Events. Algebra of events.
Week 8: Probability Theory: properties of operations with events. Frequencist, Classical and Probability Axiomatic Definition (fundamental theorems).
Week 9: Conditional Probability. Bayes Theorem. Independent events.
Week 10: Probability Theory (cont.)
Week 11: Random variables - definition and classification of variables.
Week 12: Distribution Function. Practical applications.
Week 13: Probability function. Probability density function. Practical applications.
Week 14: Expected value. Variance and standard deviation. Practical applications of the measures studied.
Week 15: Practical applications.
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) of the U.C. contributes point 1) of the program. This allows the student to recall the basic concepts of Descriptive Statistics to analyze, reduce, and interpret data.
For the purpose of ii) U.C. contributes 2.1) program. This allows analysis calculation for subsequent application in solving probability problems. The learning of probability theory concepts, sections 2.2) and 2.3) of the program allows students to obtain a solid knowledge in this area (Objective III). With regard to objectives iv) and v) directly contribute points 3) and 4)of the program.
Furthermore, the contents of this U.C. allows students to create practical skills by performing various practical exercises, to perform statistical inferences within the U.C that succeed.
Demonstration of the teaching methodologies coherence with the curricular unit's objectives
In the lecture presents the basic theoretical concepts for students to become able to apply the probability theory techniques. Practical classes allow solving practical exercises and study of practical applications of interest, with the completion of critical interpretation and analysis of the results.
|relevant generic skill||improved?||assessed?|
|Achieving practical application of theoretical knowledge||Yes||Yes|
|Adapting to new situations||Yes||Yes|
|Analytical and synthetic skills||Yes||Yes|
|Balanced decision making||Yes||Yes|
|Commitment to quality||Yes||Yes|
|Ethical and responsible behaviour||Yes|
|Event organization, planning and management||Yes||Yes|
|Foreign language proficiency|
|Information and learning management||Yes|
|IT and technology proficiency||Yes|
|Problem Analysis and Assessment||Yes||Yes|
|Relating to others||Yes|
|Written and verbal communications skills||Yes||Yes|