Using linear regression to compare assay calibrator results, if the slope is 0.99 and the y intercept is 110%, what type of error is present?

The scenario describes a situation where the slope of the linear regression is close to 1 (0.99), indicating that there is a strong correlation between the assay calibrator results and the expected values, which suggests good linearity. However, the y-intercept of 110% indicates that there is a consistent offset between the measured values and the true values. This offset is indicative of a systematic error, which occurs when results are consistently above or below the true values across all levels of measurement.

A y-intercept that is significantly different from 0 reflects a constant addition or subtraction from the measurement, which is a hallmark of a constant systematic error. This type of error does not change with the magnitude of the assay results and maintains a consistent offset—in this case, the results are consistently higher than expected by 10%.

Understanding the kinds of errors is crucial in assay calibration and ensuring the accuracy of results. In this case, the presence of a constant systematic error implies that irrespective of the actual value being measured, there is an unwavering 10% overestimation inherent in the assay due to the y-intercept.