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MULTI Online

Course dedicated to Multivariate Analysis

To request registration or for information

Goals:

Know the main statistical techniques for the analysis of multidimensional phenomena, identify the optimal technique in relation to the type of data and the objectives of the analysis, interpret the results appropriately.

 

Techniques presented:

Multivariate statistical analysis techniques for segmentation (cluster analysis), classification (discriminant analysis, decision trees) and perceptual mapping (factor analysis, correspondence analysis, multidimensional scaling).

Cost:

The cost of the entire MULTI Online course (5 sessions, for a total of 15 hours) is 750.00 Euros (excluding VAT) per participant.

 

Tutorials:

There are exercises for each of the topics covered.

 

Prerequisites:

Attendance to the course is preparatory TESTS Online(or to a previous session of the TSC calendar course) or have knowledge of the topics contained therein.

Duration:

5 sessions of 3 hours, for a total of 15 hours. A break of about 15 minutes is foreseen in the middle of each session.

Frequency:

Our online courses are delivered in live mode in order to guarantee the maximuminteractionand collaboration betweenprofessorAndparticipants. For this reason, the presence of the participants in all lessons is considered essential.

In case of absence from a lesson, the Training Staff will send the learner the points and exercises covered during the skipped lesson.

If you miss more than one lesson, the Training Staff reserves the right not to send the student the certificate of participation.

Certificate of attendance:

At the end of the course the certificate of participation will be issued.

Topics that will be addressed for each session:

First Session (3 hours)

Segmentation models (part 1)

  • Introduction to qualitative and quantitative phenomena

  • The interdependence between the variables

    • The distances

Segmentation exercises

Second session (3 hours)

Segmentation models (part 2)

  • cluster analysis

    • Hierarchical methods

    • The method of k means

    • Example of application of Cluster Analysis

Segmentation exercises

 

Third session (3 hours)

Reduction techniques

  • The factor analysis

    • Steps in performing factor analysis

    • Application example of Principal Components Analysis

  • Correspondence analysis

    • The dimensions and the perceptual map

Exercises on reduction techniques

Fourth session (3 hours)

Perceptual mapping and scaling models

  • The optimal scaling

    • The homogeneity analysis

  • Scaling  multidimensional

    • The MDS

Exercises on perceptual mapping and scaling models

Fifth session (3 hours)

Unsupervised learning techniques

  • Introduction to decision trees

    • CHAID algorithm

    • C&RT algorithm

Exercises on decision trees

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