The preferred method of data analysis of quantitative experiments is the method of
least squares. Often, however, the full power of the method is overlooked and very few
books deal with this subject at the level that it deserves. The purpose of Data Analysis
Using the Method of Least Squares is to fill this gap and include the type of
information required to help scientists and engineers apply the method to problems in
their special fields of interest. In addition, graduate students in science and
engineering doing work of experimental nature can benefit from this book. Particularly,
both linear and non-linear least squares, the use of experimental error estimates for data
weighting, procedures to include prior estimates, methodology for selecting and
testing models, prediction analysis, and some non-parametric methods are discussed.
Table of contents
Introduction.
The Method of Least Squares.
Model Evaluation.
Candidate Predictors.
Designing Quantitative Experiments.
Software.
Kernel Regression.
250 pages, 58 illus., Softcover
Written for: Engineers, scientists, graduate students in quantitative disciplines
Keywords: Curve Fitting, Data Analysis, Least Squares, Nonlinear
Models, Parametric Models