|Kurskode||0K2K9NO||Leveringsform||Self-paced Virtual Class|
|Listepris||Set by Partner|
ONCE YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. You will then have a 30 day window in which to complete your course. Within this 30 days, you will have 336 hours of elab time. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day, but lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Once you have accessed the course, contact your provider for Level 1 and Level 2 support.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of ""Introduction to IBM SPSS Decision Trees"" classroom course. Introduction to IBM SPSS Decision Trees is a two day self-paced training 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.
You should have:
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.