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Statistics 2017/2018

  • 5 ECTS
  • Taught in Portuguese
  • Both continuous and final Assessment


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.

Recommended Prerequisites

Basic knowledge of Mathematics and Descriptive Statistics.

Teaching Metodology

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.

Recommended Bibliography

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.

Complementary Bibliography

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.

Weekly Planning

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 skillimproved?assessed?
Achieving practical application of theoretical knowledgeYesYes
Adapting to new situationsYesYes
Analytical and synthetic skillsYesYes
Balanced decision makingYesYes
Commitment to qualityYesYes
Ethical and responsible behaviourYes 
Event organization, planning and managementYesYes
Foreign language proficiency  
Information and learning managementYes 
IT and technology proficiency  
Problem Analysis and AssessmentYesYes
Relating to othersYes 
Research skillsYes 
Written and verbal communications skillsYesYes
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