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Advanced Statistical Analysis Using IBM SPSS Statistics (V26) (0G09BG)

Detaillierter Kursinhalt

Introduction to advanced statistical analysis • Taxonomy of models • Overview of supervised models • Overview of models to create natural groupings Grouping variables with Factor Analysis and Principal Components Analysis • Factor Analysis basics • Principal Components basics • Assumptions of Factor Analysis • Key issues in Factor Analysis • Use Factor and component scores Grouping cases with Cluster Analysis • Cluster Analysis basics • Key issues in Cluster Analysis • K-Means Cluster Analysis • Assumptions of K-Means Cluster Analysis • TwoStep Cluster Analysis • Assumptions of TwoStep Cluster Analysis Predicting categorical targets with Nearest Neighbor Analysis • Nearest Neighbors Analysis basics • Key issues in Nearest Neighbor Analysis • Assess model fit Predicting categorical targets with Discriminant Analysis • Discriminant Analysis basics • The Discriminant Analysis model • Assumptions of Discriminant Analysis • Validate the solution Predicting categorical targets with Logistic Regression • Binary Logistic Regression basics • The Binary Logistic Regression model • Multinomial Logistic Regression basics • Assumptions of Logistic Regression procedures • Test hypotheses • ROC curves Predicting categorical targets with Decision Trees • Decision Trees basics • Explore CHAID • Explore C&RT • Compare Decision Trees methods Introduction to Survival Analysis • Survival Analysis basics • Kaplan-Meier Analysis • Assumptions of Kaplan-Meier Analysis • Cox Regression • Assumptions of Cox Regression Introduction to Generalized Linear Models • Generalized Linear Models basics • Available distributions • Available link functions Introduction to Linear Mixed Models • Linear Mixed Models basics • Hierarchical Linear Models • Modeling strategy • Assumptions of Linear Mixed Models