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.

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.

The learning process will be eased  for students already familiar with basic data analysis techniques and basic linear algebra.

1.  Knowledge and under standing. The student will know the main multivariate statistical methods (multivariate regression, prncipal components, cluster analysis, multidimensional scaling,...) and their use in marketing strategies (market segmentation, perceptual maps,...).

2.  Applying knowledge and understanding. The student will be able to explore complex data sets and detect their latent structures. More precisely, the use of case studies will help her/him to address the difficulties of the application of statistical methods to marketing research.

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. He/She will also be able to use statistical methods in a professional way, to choose between different alternatives.

4.  Communication skills. The student will learn to communicate the results of the exploratory analyses by means of graphs, tables, slides and reports. In particular, he/she will also be able to communicate the results of statistical analyses to people with little or no background in Statistics.

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. He/She will also learn to improve personal knowledge of multivariate statistical methods by self-teaching.          

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

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.

Teaching

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.

Innovative teaching methods

Flipped classroom

Attendance

Class attendance is not mandatory but is recommended.

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

The exam includes exercises to be solved. Each exercise recalls a concept, which must be known to obtain the correct solution (knowledge and understanding). Solution also require the knowledge of the appropriate operational rules (applying knowledge and understanding). The choice between different solving methods depends on the information available to the student, who must use them critically (making judgments). Effective communication of the solutions requires the communication skill taught in the course (communication skills). The efficiency in solving the exercises will be increased if the student will adapt to her/his own personal features the solution method taught in the course (learning skills).  

The final marks might also consider eventual in-course assessments. However, the in-course assessments do not replace the final exam.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

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.

Attendance

Class attendance is not mandatory but is recommended.

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

The exam includes exercises to be solved. Each exercise recalls a concept, which must be known to obtain the correct solution (knowledge and understanding). Solution also require the knowledge of the appropriate operational rules (applying knowledge and understanding). The choice between different solving methods depends on the information available to the student, who must use them critically (making judgments). Effective communication of the solutions requires the communication skill taught in the course (communication skills). The efficiency in solving the exercises will be increased if the student will adapt to her/his own personal features the solution method taught in the course (learning skills).  

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

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