Calibration: Detection, Quantification, and Confidence Limits Are (Almost) Exact When the Data Variance Function Is Known.

Inverse variance weighting ensures optimal parameter estimation in least-squares fitting, with exact parameter standard errors for linear least-squares with known data variance. In this Feature, I emphasize the virtues of numerical methods for estimating data variance functions and for determining these limits for any calibration model, linear or nonlinear.