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.
Introduction to advanced statistical analysis
• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings
Group 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 scores
Group 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 Analysis
Predict categorical targets with Nearest Neighbor Analysis
• Nearest Neighbor Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit
Predict 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 solution
Predict categorical targets with Logistic Regression
• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Testing hypotheses
Predict categorical targets with Decision Trees
• Decision Trees basics
• Validate the solution
• Explore CHAID
• Explore CRT
• Comparing 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
• Hierachical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models
Join our public courses in our Istanbul, London and Ankara facilities. Private class trainings will be organized at the location of your preference, according to your schedule.