Prediction Mathematical Models

Category: Discipline
  • Cowpertwait, P. & Metcalfe, A. (2009). Introductory time series with R . New York: Springer. Maindonald, J. & Braun, W. J. (2010). Data analysis and graphics using R: an example-based approach. (3rd ed.). United Kingdom: Cambridge University Press. Tabachnick, B. & Fidell, L. (2012) Using multivariate statistics . (6ª ed.) Boston: Pearson Education. Tufféry, S. (2011). Data mining and statistics for decision making . United Kingdom: John Wiley & Sons Ltd.    
  • Business Management
  • 490
  • 22680
  • Prediction Mathematical Models
  • ISMAT490-22680
  • 3
  • 5
  • 0
  • 12
  • Não
  • Português
  • Active, problem-solving-oriented methodologies (PBL) are used for all content.
  • Mandatory
  • At the end of this Curricular Unit, students should be able to: LO1: Identify appropriate techniques for making data-based predictions; LO2: Create, manipulate and reduce the dimensionality of data; LO3: Apply cluster analysis techniques; LO4: Develop and compare predictive models of regression and classification; LO5: Model time series and use the models for predictive purposes; LO6: Use softwares (R and jamovi) for model development and forecasting; LO7: Critically analyze the predictions obtained, taking into account the context of the problems.  
  • S1: Introduction to data-based forecasting models   S2: Pre-processing and data dimensionality reduction Collection and manipulation of data Principal components and factor analysis Reliability and validity of scales   S3: Cluster analysis Hierarchical methods of cluster analysis Non-hierarchical methods of cluster analysis   S4: Predictive models of classification and regression Linear and logistic regression Classification and regression trees Comparison of performance of predictive models   S5: Time Series Notation and nomenclature Trend and seasonality Moving averages and exponential smoothing Identification, estimation, diagnosis and prediction with ARIMA and SARIMA models   S6: Business Management applications using data analysis softwares (R and jamovi).  

  • Descrição

    Data limite

    Ponderação

    Teste de avaliação

    04-12-2025

    40%

    Trabalho de grupo

    08-01-2026

    40%

    Fichas no Moodle

    30-11-2025

    20%

     

     
  • Semestral
  • The increasing amount of data available to organizations and its importance for making decisions based on evidence, make it essential to master statistical methods, as well as computational resources that allow them to be implemented. This Curricular Unit (UC) addresses several statistical methods, which allow analyzing dependency and interdependence relationships between economic, environmental, social, marketing variables, among others, and developing predictive models. There are frequent situations in which decision-making depends on the analysis of the evolution of sets of observations made over time (time series), which is why methods of analyzing time series are also addressed in this UC.  

English Menu

Pesquisa

Login Form