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Introduction to IBM SPSS Decision Trees (V19) (0G2K9G)

Course Description Schedule Course Outline
 

Who should attend

This intermediate course is for:

  • Analysts building prediction or decision models for which many predictor variables of different types may be involved
  • Survey and Market researchers who need to perform automated decision or segmentation analysis

Prerequisites

You should have:

  • Familiarity with the Windows interface
  • Knowledge of basic statistics through regression (topics covered in Statistical Analysis Using SPSS) is very useful.

Those with advanced statistical training in predictive models (for example discriminant, logistic regression covered in Advanced Statistics Using SPSS for Windows or Market Segmentation Using SPSS) will gain more from the seminar.

Course Content

Introduction to IBM SPSS Decision Trees is a two day instructor led classroom course that covers the principles and practice of the tree-based decision and regression methods available in IBM SPSS Decision Trees. A general introduction to the features of the IBM SPSS Decision Trees module and an overview of decision tree based methods will be covered. These methods (CHAID, Exhaustive CHAID, CRT, and QUEST) are used to perform classification, segmentation, and prediction modeling in a wide range of business and research areas. The techniques are discussed and compared, analyses are performed, and the results interpreted.

Classroom Training

Duration 2 days

Price (excl. tax)
  • Germany: 1,490.- €
incl. catering
Catering includes:

  • Coffee, Tea, Juice, Water, Soft drinks
  • Pastry and Sweets
  • Bread
  • Fresh fruits
  • Lunch in a nearby restaurant

* Catering information only valid for courses delivered by Fast Lane.


Digital courseware Dates and Booking
 
Click on town name to bookSchedule
Germany
23/01/2017 - 24/01/2017 Munich
20/03/2017 - 21/03/2017 Düsseldorf
15/05/2017 - 16/05/2017 Berlin
10/07/2017 - 11/07/2017 Frankfurt
02/11/2017 - 03/11/2017 Hamburg
Austria
23/01/2017 - 24/01/2017 Vienna (iTLS)