Straight-Line Errors-In-Variables Calibration Model Versus Linear Regression Model

The aim of this article is to determine the circumstances under which the errors-in-variables regression model, utilized as a straight-line calibration model with measurement errors in both the stimulus and response variables, can be deemed a traditional linear regression model. Additionally, we strive to establish the guidelines for utilizing the locally best linear unbiased estimators (BLUEs) in a secure and optimal manner to estimate the model parameters, their covariance matrix, and confidence intervals for all feasible linear parameter combinations such that these estimators ensure reliable inference.