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Predictive Analytics in Marketing

Objectives

1. Be able to select and apply predictive analytical methods adequate for different marketing problems

2. Be able to develop hypotheses testing in marketing

3. Be able to develop and interpret the results of multiple regression analysis

4. Be able to develop and interpret the results of regression models for categorical dependent variables

5. Be able to develop and interpret the results of multiple regression analysis based on factors

6. Be able to develop and interpret the results of structural equation models (SEM)
 

General characterization

Code

200190

Credits

7.5

Responsible teacher

Docente a designar

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

    

Bibliography

- Greene, W. H. (2008) Econometric Analysis , Sixth edition. New Jersey: Prentice-Hall, Inc.
- Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (2010). Multivariate data analysis. Seventh edition, Upper Saddle River, NJ: Pearson Prentice Hall
- Hair, J. F., Hult G.T., Ringle C.M., & Sartedt M. (2016) A primer on partial least squares structural equation modeling (PLSSEM). Sage Publications
- Long J. S. (1997). Regres sion Models for Categorical and limited Dependent Variables: Sage Publications.
- Sharma, S., (1996) Applied Multivariate Techniques, John Wiley & Sons
- Vilares, J. M. & Coelho P. S. (2005) Satisfação e Lealdade do Cliente: Metodologias de avaliação, Gestão e Análise . Lisboa: Escolar Editora

Teaching method

  

Evaluation method

  

Subject matter

1. Hypotheses testing

2. Multiple regression analysis

3. Regression models for categorical dependent variables (probit/logit)

4. Multiple regression analysis based on factors

5. Structural equation models (SEM)