|Kurskode||0K093NO||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, help is available Monday through Friday; questions will be responded to within 24 hours.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of ''Advanced Statistical Analysis Using IBM SPSS Statistics'' classroom course Advanced Statistical Analysis Using IBM SPSS Statistics is a three day self-paced training course that provides an application-oriented introduction to the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for predicting both continuous and categorical outcomes, as well as methods to cluster cases, create statistical groupings of variables, and find similar cases using a large set of variables. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.
K-Means Cluster Analysis
TwoStep Cluster Analysis
Binary Logistic Regression
Multinomial Logistic Regression
Nearest Neighbor Analysis
Generalized Linear Models
Linear Mixed Models