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Contents

Preface

Part 1: Introduction to regression modeling and the RM4Es framework

This Part introduces the RM4Es framework for understanding model building processes, and starts our discussion with examples on simple regression.

Part 2: A RM4Es summary of simple regression

This Part covers technical details of simple regression model and discusses a research flow of building a simple regression model under the RM4Es framework.

Part 3: Evaluating outliers and a general diagnostics methodology

This part discusses our first step of evaluating estimation results for any regression models, with a focus on outliers. At the same time, we will also discuss a general diagnostics methodology for building regression models.

Part 4: Evaluating heteroscedasticity, non-normality and others

This part provides a further discussion on methods of evaluating regression estimation results, and their related computing methods.

Part 5: Using Variable Transformation to Redefine Equations

With a few examples, this part will discuss variable transformation as a common way of utilizing evaluation results to modify equation specification.

Part 6: Variable Selection and Collinearity Treatment to Redefine Equation

This part discusses a common problem called collinearity, and its impacts on model evaluation and equation specification. Some methods of variable selection will also be covered in this part.

Part 7: Logistic Regression and its RM4Es summary

Here in part 7, we introduce logistic regression and apply the same RM4Es framework to manage the processes of building logistic regressions.

Part 8: Evaluating Logistic Regression Results

This part covers the evaluation methods for building logistic regression models.

Part 9: Missing Values Treatment and Data Manipulation

Here in part 9, we discuss the issue of missing values and its impacts on our model building processes.

Part 10: Model Assessment and Model Validation

This part expands our discussion about model evaluation, with a focus on model assessment and model validation.

Part 11: Non-OLS Estimations

This part goes beyond the ordinary least squares estimation, to discuss a few advanced estimation methods such as the robust estimation method.

Part 12: Nonlinear Equations

This part covers nonlinear equations such as tree models, and their related estimation issues.

Part 13: Further Discussion on Model Explanation

This part provides a further discussion on results explanation for regression models.

Part 14: RM4Es Summary and RM4Es software introduction

Here in part 14, we summarize our modeling process and provide a RM4Es^TM solution together with an introduction of the RM4Es^TM software.

References