Measurement error in data used for regression modeling has been a known issue since the late 1800s. While early focus was on linear regression models, advances in computing have allowed for studying measurement error in nonlinear regression models. This article introduces the problem of measurement error in regression, discussing different types of measurement error and their practical implications. Ignoring measurement error when analyzing data can lead to significant consequences. The article provides detailed results for both simple and multiple linear measurement error models, as well as pointers to further literature on the topic.