ABSTRACT
Data measured with errors occur frequently in many scientific fields. Standard regression models assume that the independent variables have been measured exactly, in other words, observed without error. Those models account only for errors in the response variable. However, the presence of measurement errors in independent variables causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees using standard statistical analysis. Errors-in-variables regression models refer to regression models that account for measurement errors in the predictors.
