With an abundance of helpful examples, this text expertly presents the essentials of measurement, regression, and calibration. The book develops the fundamentals and underlying theories of key techniques in a clear, step-by-step progression, starting with standard least squares prediction of a single variable and moving on to shrinkage techniques for multiple variables. Self-contained chapters discuss methods that have been specifically developed for spectroscopy, likelihood and Bayesian inference (which may be applied to a wide range of multivariate regression problems), and Bayesian approaches to pattern recognition, among other topics. Ideal for instruction as well as for reference, Measurement, Regression, and Calibration will be a valuable addition to the bookshelves of professionals and advanced students in statistics and other pertinent fields.