Data Analysis Using the Method of Least Squares [electronic resource] : Extracting the Most Information from Experiments /
Wolberg, John.author.
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 Methods 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.
Description based upon print version of record.
Includes bibliographical references (p. [245]-248) and index.
The Method of Least Squares -- Model Evaluation -- Candidate Predictors -- Designing Quantitative Experiments -- Software -- Kernel Regression.
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 Methods 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.
Statistics .
Computational intelligence.
Physical measurements.
Measurement   .
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
Computational Intelligence.
Measurement Science and Instrumentation.