This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. 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, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
Experience with IBM SPSS Statistics (version 18 or later)Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V26) course.
IBM SPS Statistics users who want to learn advanced statistical methods to be able to better answer research questions.
Introduction to advanced statistical analysisGrouping variables with Factor Analysis and Principal Components AnalysisGrouping cases with Cluster AnalysisPredicting categorical targets with Nearest Neighbor AnalysisPredicting categorical targets with Discriminant AnalysisPredicting categorical targets with Logistic RegressionPredicting categorical targets with Decision TreesIntroduction to Survival AnalysisIntroduction to Generalized Linear ModelsIntroduction to Linear Mixed Models, Cloud Data and AI, Data and AI Top Market (AUO), Data and AI Top Portfolio (AUO)