Nonlinear Computer Modeling of Chemical and Biochemical Data

James F. Rusling et Thomas F. Kumosinski

ISBN 10: 0126044902 / ISBN 13: 9780126044904

Published by Academic Press, 1996

About this Item

Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations.

Seller Inventory # H-696-171

Bibliographic Details

• Title: Nonlinear Computer Modeling of Chemical and Biochemical Data
• Publisher: Academic Press
• Publication Date: 1996
• Language: English
• ISBN 10: 0126044902
• ISBN 13: 9780126044904
• Binding: Hardcover
• Condition: Très bon

About this title

Synopsis

Assuming only background knowledge of algebra and elementary calculus, and access to a modern personal computer, Nonlinear Computer Modeling of Chemical and Biochemical Data presents the fundamental basis and procedures of data modeling by computer using nonlinear regression analysis. Bypassing the need for intermediary analytical stages, this method allows for rapid analysis of highly complex processes, thereby enabling reliable information to be extracted from raw experimental data.By far the greater part of the book is devoted to selected applications of computer modeling to various experiments used in chemical and biochemical research. The discussions include a short review of principles and models for each technique, examples of computer modeling for real and theoretical data sets, and examples from the literature specific to each instrumental technique.The book also offers detailed tutorial on how to construct suitable models and a score list of appropriate mathematics software packages.

From the Back Cover

Assuming only background knowledge of algebra and elementary calculus, and access to a modern personal computer, Nonlinear Computer Modeling of Chemical and Biochemical Data presents the fundamental basis and procedures of data modeling by computer using nonlinear regression analysis. Bypassing the need for intermediary analytical stages, this method allows for rapid analysis of highly complex processes, thereby enabling reliable information to be extracted from raw experimental data.