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.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Please refer to course overview

  • 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


Experienced IBM professionals will benefit.




While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.


Length: 2.0 days (16 hours)


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