Training offering

Fast Lane Deutschland

IBM SPSS Statistics: Exploratory Data Analysis V19


Length: 1.0 Day
Course code: 0L009G
Delivery method: Classroom
Price: 790 EUR
This training is available on request.
Please contact us by phone or email at :
+49 40 253346-10


Advanced Statistical Analysis Using IBM SPSS Statistics is a three day instructor-led 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.


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.


Please refer to course overview for description information.


Factor Analysis

  • 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