Presenting numerous algorithms in a simple algebraic form

so that the reader can easily translate them into any computer language, this volume gives details of several methods for obtaining accurate least squares estimates. It explains how these estimates may be updated as new information becomes available and how to test linear hypotheses.

Linear Least Squares Computations features

many structured exercises that guide the reader through the available algorithms, plus a glossary of commonly used terms and a bibliography of supplementary reading ... collects "ancient" and modern results online linear least squares computations in a convenient single source ... develops the necessary matrix algebra in the context of multivariate statistics ... only makes peripheral use of concepts such as eigenvalues and partial differentiation ... interprets canonical forms employed in computation ... discusses many variants of the Gauss, Laplace-Schmidt, Givens, and Householder algorithms ... and uses an empirical approach for the appraisal of algorithms.

Linear Least Squares Computations serves as

an outstanding reference for industrial and applied mathematicians, statisticians, and econometricians, as well as a text for advanced undergraduate and graduate statistics, mathematics, and econometrics courses in computer programming, linear regression analysis, and applied statistics.