Regression in Engineering And The Applied Sciences

Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences.

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics
This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing.

This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions.
While science is about discovery, the primary paradigm of engineering and "applied science" is design.

A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression
Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis.

This text is designed for a one-semester course on Probability and Statistics. The exposition unfolds systematically from an introductory chapter to such topics as random variables and vectors, stochastic processes, estimation, testing and regression. The topics are well chosen and the presentation is enriched by many examples from real life.

This volume updates purely mathematical theoretical aspects, and it also presents concrete examples of the wide range of applications of matrix theory in other disciplines.

This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R.