IBM Course Code: 0L009
IBM SPSS Statistics: Exploratory Techniques (V19) is a one day instructor-led online course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. Students will review several 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. 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.
Who Needs to Attend
This advanced course is for:
- Anyone who has worked with SPSS Statistics and wants to become better versed in the more advanced statistical capabilities.
- Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using SPSS Statistics.
- Analysts and Modelers
You should have:
- On the job statistical experience or completion of the Introduction to Statistical Analysis Using IBM SPSS Statistics course and/or Intermediate-level statistics oriented courses.
- Knowledge of basic statistics, including linear regression.
- IBM SPSS Statistics Standard, IBM SPSS Statistics Professional, IBM SPSS Statistics Premium.
There are no follow-ons for this course.
- Explain the basic theory of factor analysis and the steps in factor analysis
- Explain the assumptions and requirements of factor analysis
- Specify a factor analysis and interpret the output
K-Means Cluster Analysis
- Explain the basic theory of cluster analysis and the steps in doing a cluster analysis
- Explain the approach of K-Means cluster analysis
- Specify a K-Means cluster analysis and interpret the output
TwoStep Cluster Analysis
- Explain the basic approach of TwoStep cluster analysis
- Specify a TwoStep cluster analysis
- Use the Model Viewer to study and interpret the output
Binary Logistic Regression
- Explain the basic theory and assumptions of logistic regression
- Specify a logistic regression analysis
- Interpret model fit, logistic regression coefficients and model accuracy