## MARKETING STATISTICAL METHODOLOGY METODI STATISTICI PER IL MARKETING

#### Marketing Statistical Methodology Metodi Statistici per il Marketing

A.Y. Credits
2015/2016 8
Lecturer Email Office hours for students
Nicola Maria Rinaldo Loperfido

### Assigned to the Degree Course

Date Time Classroom / Location

### Learning Objectives

The course teaches the main multivariate statistical methods, which have been used more and more often  in the last years. More precisely, it teaches market segmentation, perceptual maps, outlier detection, customer satisfaction and social networks.

### Program

1.  Refresh of basic Statistics. Mean, variance, skewness, kurtosis, correlation, simple linear regression, marginal frequencies, joint frequencies, conditional frequencies.

2.  Refresh of linear algebra. Vectors, matrices, relationships, operations, main examples, linear system, rank, determinant.

3.  Advanced linear algebra. Block matrices, linear spaces, orthogonal matrices, eigenvectors, eigenvalues quadratic forms, singular value decomposition, matrix approximations, products of matrices.

4.  Preliminary data analysis. Data matrix, distance matrix, mean vector, variance matrix, correlation matrix, multivariate skewness and kurtosis, multi-way arrays.

5.  Case-oriented methods. Cluster analysis, analysis of variance, discriminant analysis, multidimensional scaling.

6.  Variable-oriented methods. Multivariate regression, principal components, correspondence analysis, canonical correlations.

7.  Marketing applications: market segmentation, perceptual maps, conjoint analysis, customer satisfaction, sales predictions, social networks.

### Learning Achievements (Dublin Descriptors)

1.  Knowledge and under standing. The student will know the main multivariate statistical methods and their use in marketing strategies.

2.  Applying knowledge and understanding. The student will be able to explore complex data sets and detect their latent structures.

3.  Making judgements. The student will be able to choose the most appropriate methods for data exploration and to evaluate the quality of the obtained results.

4.  Communication skills. The student will learn to communicate the results of the exploratory analyses by means of graphs, tables, slides and reports.

5.  Learning skills. The student will be able to connect the contents of the course with the methods learnt in other courses or by self-teaching.

### Teaching Material

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

### Didactics, Attendance, Course Books and Assessment

Didactics

1.  Classes. Presentation of theory, analysis of real data sets, informal checking of learning progresses. The teaching is interactive, in order to motivate the student into active participation.

2.  Practice. Data organization and elaboration by means of the software EXCEL. More precisely, the main statistical facilities of EXCEL will be explained and used.

3.  Office hours. While classes are given, there are weekly office hours, whose time is fixed before the classes themselves. When there are no classes office hours are decided together with the student by e-mail.

Course books

1.  Notes written by the lectures. They include solutions to exercises, worked exercises, summary schemes, examination rules, fake exams.

2.  The book by Mary Fraire and Alfredo Rizzi : Analisi dei Dati per il Data Mining, editor Carocci. It will be particularly useful for the topics 4, 5, 6 and 7 listed above.

3.   The book by Alfredo Rizzi: Il Linguaggio delle Matrici, editor Carocci. It will be particularly useful for topics2 and 3 listed above.

Assessment

Written examination with exercises similar to the ones in the teaching material.

### Additional Information for Non-Attending Students

Didactics

While classes are given, there are weekly office hours, whose time is fixed before the classes themselves. When there are no classes office hours are decided together with the student by e-mail.

Course books

Notes written by the lectures. They include solutions to exercises, worked exercises, summary schemes, examination rules, fake exams.

2.  The book by Mary Fraire and Alfredo Rizzi : Analisi dei Dati per il Data Mining, editor Carocci. It will be particularly useful for the topics 4, 5, 6 and 7 listed above.

3.   The book by Alfredo Rizzi: Il Linguaggio delle Matrici, editor Carocci. It will be particularly useful for topics2 and 3 listed above.

Assessment

Written examination with exercises similar to the ones in the teaching material.

### Notes

The student can request to sit the final exam in English with an alternative bibliography.

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