Learning objectives
The course gives knowledge on statistical techniques for Marketing applications.
In particular, the course addresses:
a) multiple linear regression and its applications in Marketing;
b) logistic regression for prediction of consumer behaviour;
c) classification trees prediction of consumer behaviour and for consumer segmentation;
d) cluster analysis for consumer segmentation.
The aim of the course is threefold:
1. To provide both a theoretical and a practical understanding of the key methods of model building, classification and prediction.
2. To provide a Marketing-driven context for these methods.
3. Using real case studies and a learning-by-doing approach, to illustrate the application and the interpretation of these methods.
Computational aspects of the methods are addressed through the use of MS Excel and IBM SPSS.
Prerequisites
Knowledge of basic statistical methods, as given in undergraduate programs in Economics and Management. Knowledge of the contents of the course on "Statistical Methods for Management".
Course unit content
The aim of this course is to illustrate the main statistical techniques that are useful for Data Mining applications, with emphasis on consumer behaviour. Specifically, the course will address:
a) multiple linear regression and its applications in Marketing;
b) logistic regression for prediction of consumer behaviour;
c) classification trees prediction of consumer behaviour and for consumer segmentation;
d) cluster analysis for consumer segmentation.
The course will cover both the statistical theory behind these techniques and their application potential. Emphasis will also be placed on computational aspects, through the use of MS Excel and IBM SPSS.
Full programme
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Bibliography
M. Riani, F. Laurini, G. Morelli: Strumenti statistici e informatici per applicazioni aziendali. Pitagora Editrice, Bologna, 2013, from Chapter 3 onwards.
A. Cerioli, F. Laurini: Il modello di regressione logistica per le decisioni aziendali, Uni.Nova, Parma, 2013 (all but the Appendix).
S. Zani e A. Cerioli: Analisi dei dati e data mining per le decisioni aziendali, Giuffrè, Milano, Chapter IX (Sections 1 – 2 – 11 – 12), Chapter XI (all).
Teaching methods
Lectures; seminars of external experts; practical work.
Details on the timetable will be provided to the class and made available through the web site:
http://elly.economia.unipr.it/2016/
Assessment methods and criteria
Written exam. Knowledge and understanding will be assessed by methodological questions, marked 3 grade points each. The ability of applying knowledge and understanding will be assessed by questions on the interpretation of results, marked 3 grade points each. Learning skills will be assessed by questions on the conclusions to be drawn from an analysis, marked 3 grade points each.
Details on examination procedures will be provided to the class and made available through the web site:
http://elly.economia.unipr.it/2016/
Other information
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