- 5 ECTS
- Taught in Portuguese
- Both continuous and final Assessment
- relevant skillset
It is intended that the student is able to:
i. From the notions of Descriptive Statistics: analyze and interpret a data set (revisions) and establish the possible relationship between two sets of quantitative data to make predictions.
ii. Interpret, analyze and apply the theory of probability to various practical problems.
iii. Define, classify and use random variables
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 (revisions) :data, population/sample and varibles
1.2 Univariate Distributions (revisions): Frequency distributions, graphical representations and descriptive measures (location, dispersion).
1.3 Bivariate Distributions: correlation and simple regression
1.4 Applications (software 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.
Magalhães, F., Oliveira, C. e Silva, E. (2017). Estatística Descritiva aplicada à Gestão: uma análise exploratória dos dados. Vida Económica.
Newbold, P., Carlson, W. and Thorne, B. (2013). Statistics for Business and Economics. 8ª edição, Pearson.
Pedrosa, A. e Gama, S. (2016) Introdução Computacional à Probabilidade e Estatística. 3ª edição, Porto Editora.
Figueiredo,F., Figueiredo, A., Ramos, A. e Teles,P. (2009) Estatística Descritiva e Probabilidades, Escolar Editora.
Afonso, A. e Nunes, C. (2011). Estatística e Probabilidades. Aplicações e soluções em SPSS. 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.
Week 1: Presentation of the program content, sources of information and evaluation methods of the UC. Revision on discrete statistical variables: raw data, frequency distributons and graphical representations, descriptive measures of location and dispersion.
Week 2: Continuation of the revision of discrete statistical variables.
Week 3: revisions on continuous statistical variables: Frequency distributions and graphical representations, descriptive measures of location and dispersion.
Week 4: Scatter diagram. Covariance. Correlation. Pearson correlation coefficient.
Week 5: Simple regression. Determination coefficient. Practical aplications
Week 6: Probability Theory: random experiences; Results space; Events. Algebra of events.
Week 7: Probability Theory: properties of operations with events. Frequencist, Classical (revisions of Combinatorial analysis)
Week 8: Probability Axiomatic Definition (fundamental theorems).Practical applications
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
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 (i) it also contributes the poin 1.3 that allows to obtais a basic knowledge on bivariate 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
Expositive and participatory methods are used to achieve objectives (i) to (iii). These are achivied by combining the theoretical exposition, critical analysis and discussion of some examples and practical exercises, in a participatory manner, that is, elaborated and discussed by all.
|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|
|Problem Analysis and Assessment||Yes||Yes|
|Relating to others||Yes|
|Written and verbal communications skills||Yes||Yes|