ECTS - University of Pisa
Scheda programma d'esame
STATISTICS IN MARKET RESEARCH
LUCIO MASSERINI
Academic year2016/17
CourseMARKETING AND MARKET RESEARCH
Code212PP
Credits9
PeriodSemester 1
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
STATISTICA NELLA RICERCA DI MERCATOSECS-S/01LEZIONI63
LUCIO MASSERINI unimap
Programma non disponibile nella lingua selezionata
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.. 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