STATISTICAL METHODS FOR MARKETING DECISIONS LABORATORY

This is not a compulsory course. It provides statistical research metthds marketing evaluation and data collection statistical techniques, surveys on consumer behavior to understand market problems.
The course deals with:
1. questionnaire formulation ad administration,
2. how to collect, encode, and organize data,
3. application of multivariate statistical analysis techniques (choice discret models and conjoint analysis) through the use of open source statistical packages, such as R and Gretl.

1. Knowledge and understanding:
1.1 Ability to collect, summarize and describe qualitative and quantitative data;
1.2 Ability to understand basic statistical inference and regression models;
1.3 Ability to deal with statistical multivariate tecniques such as Principal Component Analysis and Cluster Analysis;

2. Applying knowledge and understanding:
2.1 Abilty to formulate and admisiter questionnaires (face to face, via internet...)
2.2 Ability to use open source software R;
2.3 Ability to use choice discrete models and conjoint analysis.

3. Making judgements:
3.1 Ability to apply and compare different market segmentation techinques.

The student must know the fundamentals of Statistics and the most popular multivariate analysis techniques (Principal Component Analysis and Cluster Analysis)

The course program is structured as follows:
1. formulation and administration of a questionnaire,
2. organization of collected data and basic statistical analysis with open source software,
3. reducing the dimensionality of the data matrix,with open source software.
4. discrete choice models (multinomial logit model and Covariate Uniform Binomial model),with open source software.

Book:
Frascella C. (2017) Statistica multivariata con R, Pisa University Press.
Menard S. (2001) Applied logistic regression, second edition, Sage Publications, Inc.

Papers:

Mauracher, C., Procidano, I., Sacchi, G.(2016), Wine tourism quality perception and customer satisfaction reliability: the Italian Prosecco District. Journal of Wine Research 27, 284–299.

Piccolo, D. (2015). Inferential issue on CUB models with covariates.Communications in Statistics-Theory and Methods,44, 5023

Piccolo, D., Capecchi, S., Iannario, M., & Corduas, M. (2013). Modelling consumer preferences for extravirgin olive oil: the Italian case.Politica Agricola Internazionale,1,25.

Other readings will be recommended by the teacher during the course

Grading is based on written and oral exam.The written test consists of applying to the dataset collected by the student of multivariate statistical analysis techniques and/or discrete choice models.The oral test consists in explaining results obtained.

Lectures and exercises with open source program (R e Gretl)

Italian

Accessibility, Disability and Inclusion

Accommodation and support services for students with disabilities and students with specific learning impairments



Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support

services and accommodation available to students with disabilities. This includes students with

mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

written and oral