Detailed Course Outline
Introduction to advanced statistical analysis• Taxonomy of models• Overview of supervised models• Overview of models to create natural groupingsGroup variables: Factor Analysis and Principal Components Analysis• Factor Analysis basics• Principal Components basics• Assumptions of Factor Analysis• Key issues in Factor Analysis• Improve the interpretability• Use Factor and component scoresGroup similar cases: 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 AnalysisPredict categorical targets with Nearest Neighbor Analysis• Nearest Neighbor Analysis basics• Key issues in Nearest Neighbor Analysis• Assess model fitPredict categorical targets with Discriminant Analysis• Discriminant Analysis basics• The Discriminant Analysis model• Core concepts of Discriminant Analysis• Classification of cases• Assumptions of Discriminant Analysis• Validate the solutionPredict categorical targets with Logistic Regression• Binary Logistic Regression basics• The Binary Logistic Regression model• Multinomial Logistic Regression basics• Assumptions of Logistic Regression procedures• Testing hypothesesPredict categorical targets with Decision Trees• Decision Trees basics• Validate the solution• Explore CHAID• Explore CRT• Comparing Decision Trees methodsIntroduction to Survival Analysis• Survival Analysis basics• Kaplan-Meier Analysis• Assumptions of Kaplan-Meier Analysis• Cox Regression• Assumptions of Cox RegressionIntroduction to Generalized Linear Models• Generalized Linear Models basics• Available distributions• Available link functionsIntroduction to Linear Mixed Models• Linear Mixed Models basics• Hierachical Linear Models• Modeling strategy• Assumptions of Linear Mixed Models