Error estimates on normal least squares linear regression with replicate injection of calibration standards

In bioanalysis, including replicate injection of calibration standards, a normal least squares linear regression model can be generated from, A: the first injection dataset; B: the second injection dataset; C: averaged data from both injection datasets; and D: all data from both injections. Sample results, and their estimated confidence intervals, are expected to be different among these four models.

Models C and D yield same slopes and intercepts, which are the mathematical means of respective values in models A and B. Relatively narrower confidence intervals on sample results are estimated in both models C and D as the former reduces the overall standard error of the curve and the latter increases the total number of calibration points and leads to a lower Student's t-value.

Replicate injection of calibration standards provides added benefits for an analytical measurement on instrument sensitivity compensation and relatively improved precision of results.