STATISTICS IN MARKET RESEARCH Anno accademico 2016/17

Sistema centralizzato di iscrizione agli esami
Programma
STATISTICS IN MARKET RESEARCH
LUCIO MASSERINI
Anno accademico
CdS
2016/17
MARKETING AND MARKET
RESEARCH
212PP
9
Codice
CFU
Moduli
STATISTICA NELLA
RICERCA DI MERCATO
Settore
SECS-S/01
Tipo
LEZIONI
Ore
63
Docente/i
LUCIO MASSERINI
Learning outcomes
Knowledge
The students who complete the course successfully will be able to demonstrate a basic knowledge of the principal multivariate statistical
methods used in marketing research and to apply these methods to real problems. Moreover, students will be able to choose the most suitable
analysis methods in relation to the research objectives. Knowledge will be integrated by the ability of analysing real data by using statistical
software.
Assessment criteria of knowledge
- The students will be assessed on the ability to discuss the main statistical methods from a practical and theoretical point of view, using the
appropriate formulation and terminology. - In the written exam (1 hour and thirty minutes, made up of 5 questions including at least an exercise),
the students must demonstrate their knowledge of the course material and the ability of organising an effective, complete and correctly written
reply. - With the laboratory report and discussion, students must demonstrate the ability to approach and solve an applied exercise using
statistical software and organise an effective exposition of the results.
Methods:
Final written exam
Laboratory report
Further information:
Final written exam 85%; laboratory report and discussion 15%.
Teaching methods
Delivery: face to face
Learning activities:
attending lectures
participation in seminar
preparation of oral/written report
individual study
group work
Laboratory work
ICT assisted study
Attendance: Advised
Teaching methods:
Lectures
laboratory
Syllabus
The course aims at introducing the main statistical methods used in marketing research. Particular attention will be paid to some methods of
multidimensional data analysis (principal component analysis, correspondence analysis, multidimensional scaling and cluster analysis) and
statistical models such as linear and logistic regression. A part of the course will be dedicated to case studies and exercises to be carried out in
laboratory by using statistical software.
Bibliography
Recommended reading includes the following works Zani, S., Cerioli, A.. Analisi dei dati e data mining per le decisioni aziendali. Giuffré, Milano
(2007). B. Bracalante, M. Cossignani, A. Mulas. Statistica aziendale. McGraw-Hill, Milano (2009). Further bibliography Fraire, M., Rizzi, A..
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Sistema centralizzato di iscrizione agli esami
Programma
Analisi dei dati per il Data Mining. Carocci, Roma (2006). De Lillo, A., Argentin, G., Lucchini, M., Sarti, S., Terraneo, M.. Analisi multivariata per
le scienze sociali. Pearson (2007). Molteni, L., Troilo, G.. Ricerche di marketing. McGraw-Hill (2007).
Updated: 14/11/2016 17:27
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