What you’ll learn
Perform advanced linear regression using predictor selection techniques
Perform any type of nonlinear regression analysis
Make predictions using the k nearest neighbor (KNN) technique
Use binary (CART) trees for prediction (both regression and classification trees)
Use non-binary (CHAID) trees for prediction (both regression and classification trees)
Build and train a multilayer perceptron (MLP)
Build and train a radial basis funcion (RBF) neural network
Perform a two-way cluster analysis
Run a survival analysis using the Kaplan-Meier method
Run a survival analysis using the Cox regression
Validate the predictive techniques (KNN, trees, neural networks) using the validation set approach and the cross-validation
Save a predictive analysis model and use it for predictions on future new data
“Getting Started
Introduction
“Advanced Linear Regression Techniques
Introduction to Stepwise Regression
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
Comparing Nested Models Using the Remove Method
Executing the Regression Analysis with the Remove Method
Interpreting the Results of the Remove Method
“Nonlinear Regression Analysis
Types of Nonlinear Functions
An Important Classification of the Nonlinear Relationships
Performing a Nonlinear Regression With an Exponential Relationship
Performing a Nonlinear Regression With a Logistic Relationship
“K Nearest Neighbor in SPSS
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
“Introduction to Decision Trees
What Are Decision Trees?
Binary Trees (CART)
Non-Binary Trees (CHAID)
Advantages and Disadvantages of Decision Trees
“Growing Binary Trees (CART) in SPSS
Growing a Binary Regression Tree (CART)
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
Interpreting the Cross-Validation Results for a Classification Tree
Using Binary Trees for Future Predictions
“Growing Non-Binary Trees (CHAID) in SPSS
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
“Introduction to Neural Networks
The Architecture of an Artificial Neural Network
What Happens Inside of a Neuron?
Activation Functions
Neural Network Learning Process
“Training a Multilayer Perceptron (MLP) in SPSS
Building a Multilayer Perceptron
Interpreting the Multilayer Perceptron
Interpreting the ROC Curve
Using the Multilayer Perceptron for Future Predictions
“Training a Radial Basis Function (RBF) Neural Network in SPSS
Building an RBF Neural Network
Interpreting the RBF Network
Using the RBF Network for Future Predictions
Two-Step Cluster Analysis
What is Two-Step Clustering?
Executing the Two-Step Cluster Analysis
Examining the Evaluation Variables
Using Your Clustering Model for Future Predictions
Survival Analysis
What Is the Survival Analysis?
Introduction to the Kaplan-Meier Method
Introduction to the Cox Regression
Our Practical Example
Executing the Kaplan-Meier Procedure
Executing the Cox Regression
Interpreting the Cox Regression
Practical Exercises
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July 4, 2020
July 4, 2020
July 4, 2020