Linear Models and Matrix Least Squares in Clinical Chemistry

Abstract

Clinical chemists are often required to fit mathematical models to experimental data. In some studies, the individual model parameters and their uncertainties are of primary importance; for example, the initial reaction rate (slope with respect to time) of a kinetic method of analysis (1). In other studies, the graph of the whole model (and the associated uncertainty) is of interest—e.g., a calibration curve relating measured values of response to a property of a material (2). In still other studies, statistical measures of how well the model fits the data are desired; the correlation coefficient obtained in methods comparison studies is an example (3).