The omitted variable bias is a common and serious problem in regression analysis. Generally, the problem arises if one does not consider all relevant variables in a regression. In this case, one violates the third assumption of the assumption of the classical linear regression model. The following blog posts explain the omitted variable bias and demonstrate its consequences.

### Introduction

The first post explains the omitted variable bias in general words and introduces an example that we use throughout the series.

### Understanding the Bias

The second post describes the nature of the omitted variables bias by the means of a Venn-Diagram. This post really tries to increase the general understanding of the bias and to provide an deeper intuition on the dynamics of the bias.

### Explanation and Example

The third post continues to work on understanding of the bias. While the third post provides an understanding in more general terms, this post addresses the omitted variable bias in formal way. Using our working example, the post will detail what exactly happens to our estimates when we neglect a variable.

### Consequences

The fourth post elaborates the consequences of the omitted variable bias. That is, the post shows that omitting a variable form the regression model violates the third OLS assumption and discusses what will happen if this assumption is violated.

### What can we do about it?

The fifth post summarizes what one can do in the light of an omitted variable bias. The post lists several points how one can address an omitted variable bias.

### Concluding Remarks

The last post concludes. It provides a short summary of the omitted variable bias and presents a list of questions, regarding the omitted variable bias, which one should answer before conducting a linear regression analysis.

###### Omitted Variable Bias

- Overview
- Introduction
- Understanding the Bias
- Explanation and Example
- Consequences
- What can we do about it?
- Concluding Remarks

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