Modules

Table of Contents

16. Automatic Variable Selection

Introduction

Method description

Method assumptions

Full Model

Forward Selection

Backward Elimination

Stepwise Selection

Best Subset Selection

Usage

Data requirements

Model building and testing

Model application

Example of automatic variable selection

References

17. Bivariate Probit

Introduction

Method description

Full observability likelihood function

Partial observability likelihood function

Maximum likelihood estimator

Model significance

Testing for zero correlation

Confidence limits

Usage

Data requirements

Model building

Model application

Example

References

18. Classification Trees

Introduction

Method description

The structure of Classification Trees

Tree building algorithm

Tree pruning

Null values

Usage

Data requirements

Model building and testing

Model application

Model statistics

Example

References

19. Smart Trees

Introduction

Method description

The structure of Smart Trees

Model building algorithm

Null values

Usage

Data requirements

Model building and testing

Model statistics

20. Discriminant Analysis

Introduction

Method description

The discriminant analysis model

Model assumptions

Usage

Data requirements

Model building and testing

Model application

Example

References

21. Matching (Data Quality)

Introduction

Method description

Blocking indexes

Attribute similarity evaluations

Record classification

Usage

Features

Data requirements

Model building and testing

Model Application

Example

References

22. Feed Forward Neural Networks

Introduction

Method description

Usage

Data requirements

Model building and testing

Model application

Examples

Data preparation

Model building examples

Model application examples

Model testing examples

References

23. K-Means Clustering

Introduction

Method description

Usage

Data requirements

Model building

Model statistics

Model application

Example of K-Means Clustering

References

24. Kohonen Networks

Introduction

Method description

Usage

Data requirements

Model building

Computation of model statistics

SOM Explorer

The SOM Model

Visualization

Saving a modified model

Examples

References

25. Linear Regression

Introduction

Method description

Standard linear regression

Weighted Linear Regression (WLS)

Iteratively Re-Weighted Least Squares (IRLS) Regression

Usage

Data requirements

Model building and testing

Model application

Examples

Standard linear regression example

IRLS regression example

References

26. Logistic Regression

Introduction

Method description

The logit function

Odds and odds ratio

Likelihood function

Measures of goodness of fit of the model

Multicollinearity in Logistic Regression

Confidence intervals

Usage

Data requirements

Model building and testing

Model application

Example of logistic regression

References

27. Survival Analysis

Introduction

Method description: survival models

Censored observations

Nonparametric models

The Cox model

Usage

Data requirements

Model building and testing

Model application

Example of Survival Analysis

Cox semi-parametric model example

Non-parametric survival model example

References

28. Scoring Card

Introduction

Method description

Definitions and notation

Algorithm details

Usage

Data requirements

Model building

Model testing

Model application

Examples

Creating a scoring card using the provided context script

References

29. Time Series

Introduction

Method description

Usage

Data requirements

Model building

Model testing

Model application

Examples

Model building

Model testing

Model application

References

30. Social Network Analysis Module

Introduction

Method description

Social Network

Classification of networks

Basic concepts used in network analysis

Description of the algorithms used in the Social Network Analysis

Usage

Network building

Network analysis

Network filtering

Network visualization

Examples

References