# Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

Durée:

2

Langue:

de

Prix:
1583.6
Description:

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.

â¢ Introduction to advanced statistical analysisÂ
â¢ Grouping variables with Factor Analysis and Principal Components AnalysisÂ
â¢ Grouping cases with Cluster AnalysisÂ
â¢ Predicting categorical targets with Nearest Neighbor AnalysisÂ
â¢ Predicting categorical targets with Discriminant AnalysisÂ
â¢ Predicting categorical targets with Logistic RegressionÂ
â¢ Predicting categorical targets with Decision TreesÂ
â¢ Introduction to Survival AnalysisÂ
â¢ Introduction to Generalized Linear ModelsÂ
â¢ Introduction to Linear Mixed Models

IBM SPS Statistics users who want to learn advanced statistical methods to be able to better answer research questions.

• Experience with IBM SPSS Statistics (version 18 or later)Â
• 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.Â

â¢ Taxonomy of modelsÂ
â¢ Overview of supervised modelsÂ
â¢ Overview of models to create natural groupingsÂ

Grouping variables with Factor Analysis and Principal Components AnalysisÂ
â¢ Factor Analysis basicsÂ
â¢ Principal Components basicsÂ
â¢ Assumptions of Factor AnalysisÂ
â¢ Key issues in Factor AnalysisÂ
â¢ Use Factor and component scoresÂ

Grouping cases with Cluster AnalysisÂ
â¢ Cluster Analysis basicsÂ
â¢ Key issues in Cluster AnalysisÂ
â¢ K-Means Cluster AnalysisÂ
â¢ Assumptions of K-Means Cluster AnalysisÂ
â¢ TwoStep Cluster AnalysisÂ
â¢ Assumptions of TwoStep Cluster AnalysisÂ

Predicting categorical targets with Nearest Neighbor AnalysisÂ
â¢ Nearest Neighbors Analysis basicsÂ
â¢ Key issues in Nearest Neighbor AnalysisÂ
â¢ Assess model fitÂ

Predicting categorical targets with Discriminant AnalysisÂ
â¢ Discriminant Analysis basicsÂ
â¢ The Discriminant Analysis modelÂ
â¢ Assumptions of Discriminant AnalysisÂ
â¢ Validate the solutionÂ

Predicting categorical targets with Logistic RegressionÂ
â¢ Binary Logistic Regression basicsÂ
â¢ The Binary Logistic Regression modelÂ
â¢ Multinomial Logistic Regression basicsÂ
â¢ Assumptions of Logistic Regression proceduresÂ
â¢ Test hypothesesÂ
â¢ ROC curvesÂ

Predicting categorical targets with Decision TreesÂ
â¢ Decision Trees basicsÂ
â¢ Explore CHAIDÂ
â¢ Explore C&RTÂ
â¢ Compare Decision Trees methodsÂ

Introduction to Survival AnalysisÂ
â¢ Survival Analysis basicsÂ
â¢ Kaplan-Meier AnalysisÂ
â¢ Assumptions of Kaplan-Meier AnalysisÂ
â¢ Cox RegressionÂ
â¢ Assumptions of Cox RegressionÂ

Introduction to Generalized Linear ModelsÂ
â¢ Generalized Linear Models basicsÂ
â¢ Available distributionsÂ