Introduction

Building an RBF Neural Network

Interpreting the RBF Network

Using the RBF Network for Future Predictions

What is Two-Step Clustering

Executing the Two-Step Cluster Analysis

Interpreting the Output of the Two-Step Cluster Analysis (1)

Interpreting the Output of the Two-Step Cluster Analysis (2)

Examining the Evaluation Variables

Using Your Clustering Model for Future Predictions

What Is the Survival Analysis

Introduction to the Kaplan-Meier Method

Introduction to the Cox Regression

Our Practical Example

Executing the Kaplan-Meier Procedure

Interpreting the Results of the Kaplan-Meier Method (1)

Interpreting the Results of the Kaplan-Meier Method (2)

Executing the Cox Regression

Interpreting the Cox Regression

Introduction to Stepwise Regression

Executing the Regression Analysis with the Remove Method

Interpreting the Results of the Remove Method

Our Practical Example

Executing the Stepwise Regression Method

Interpreting the Results of the Stepwise Method

Executing the Forward Selection Regression

Interpreting the Results of the Forward Selection Method

Executing the Backward Selection Regression

Interpreting the Results of the Backward Selection Method

Types of Nonlinear Functions

Performing a Nonlinear Regression With a Logistic Relationship

An Important Classification of the Nonlinear Relationships

Performing a Quadratic Regression in SPSS (1)

Performing a Quadratic Regression in SPSS (2)

Performing a Cubic Regression in SPSS (1)

Performing a Cubic Regression in SPSS (2)

Performing an Inverse Regression in SPSS (1)

Performing an Inverse Regression in SPSS (2)

Performing a Nonlinear Regression With an Exponential Relationship

Introduction to K Nearest Neighbor (KNN)

Selecting the Optimal Number of Neighbors

Our Practical Example

Performing the KNN technique

Interpreting the results of the KNN analysis

Finding the Optimal Number of Neighbors with Cross-Validation

Interpreting the Cross-Validation Results

Using the KNN Model for Future Predictions

What Are Decision Trees

Binary Trees (CART)

Non-Binary Trees (CHAID)

Advantages and Disadvantages of Decision Trees

Growing a Binary Regression Tree (CART)

Interpreting the Cross-Validation Results for a Classification Tree

Using Binary Trees for Future Predictions

Intepreting a Binary Regression Tree (1)

Intepreting a Binary Regression Tree (2)

Computing the R Squared

Growing a CART Regression Tree with Cross-Validation

Interpreting the Cross-Validation Results for a Regression Tree

Growing a CART Classification Tree in SPSS

Interpreting the CART Classification Tree

Growing a CART Classification Tree with Cross-Validation

Building a CHAID Regression Tree

Interpreting a CHAID Regression Tree

Growing a CHAID Regression Tree with Cross-Validation

Building a CHAID Classification Tree

Interpreting a CHAID Classification Tree

Growing a CHAID Classification Tree with Cross-Validation

Using Non-Binary Trees for Future Predictions

The Architecture of an Artificial Neural Network

What Happens Inside of a Neuron

Activation Functions

Neural Network Learning Process

Building a Multilayer Perceptron

Interpreting the Multilayer Perceptron

Interpreting the ROC Curve

Using the Multilayer Perceptron for Future Predictions