Nonlinear computer modeling of chemical and biochemical data

• Main Authors: Rusling, James F, Kumosinski, Thomas F
• Format: eBook Book
• Language: English
• Published: San Diego Academic Press 1996; Elsevier Science & Technology
• Edition: 1
• Subjects: Chemistry; Chemistry > Statistical methods > Data processing; Data processing; Nonlinear theories; Regression analysis; Regression analysis > Data processing; Statistical methods
• ISBN: 9780126044904; 0126044902

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.

Table of Contents:

• Chapter 12. Chronocoulometry -- A. Basic Principles -- B. Estimation of Diffusion Coefficients -- C. Surface Concentrations of Adsorbates from Double Potential Steps -- D. Rate Constant for Reaction of a Product of an Electrochemical Reaction -- References -- Chapter 13. Automated Resolution of Multiexponential Decay Data -- A. Considerations for Analyses of Overlapped Signals -- B. Automated Analysis of Data with an Unknown Number of Exponentials -- References -- Chapter 14. Chromatography and Multichannel Detection Methods -- A. Overlapped Chromatographic Peaks with Single-Channel Detection -- B. Multichannel Detection -- References -- Appendix I. Linear Regression Analysis -- Index
• Front Cover -- Nonlinear Computer Modeling of Chemical and Biochemical Data -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Part I: General Introduction to Regression Analysis -- Chapter 1. Introduction to Nonlinear Modeling of Data -- A. What Is Nonlinear Modeling? -- B. Objectives of This Book -- References -- Chapter 2. Analyzing Data with Regression Analysis -- A. Linear Models -- B. Nonlinear Regression Analysis -- C. Sources of Mathematics Software Capable of Linear and Nonlinear Regression -- References -- Chapter 3. Building Models for Experimental Data -- A. Sources of Data and Background Contributions -- B. Examples of Model Types -- C. Finding the Best Models -- References -- Chapter 4. Correlation between Parameters and Other Convergence Problems -- A. Correlations and How to Minimize Them -- B. Avoiding Pitfalls in Convergence -- References -- Part II: Selected Applications -- Chapter 5. Titrations -- A. Introduction -- References -- Chapter 6. Macromolecuiar Equilibria and Kinetics: Linked Thermodynamic Models -- A. The Concept of Linked Functions -- B. Applications of Thermodynamic Linkage -- References -- Chapter 7. Secondary Structure of Proteins by Infrared Spectroscopy -- A. Introduction -- B. Analysis of Spectra-Examples -- References -- Chapter 8. Nuclear Magnetic Resonance Relaxation -- A. Fundamentals of NMR Relaxation -- B. Applications from NMR in Solution -- C. Applications from NMR in the Solid State -- References -- Chapter 9. Small-Angle X-Ray Scattering (SAXS) of Proteins -- A. Theoretical Considerations -- B. Applications -- References -- Chapter 10. Ultracentrifugation of Macromolecules -- A. Sedimentation -- References -- Chapter 11. Voltammetric Methods -- A. General Characteristics of Voltammetry -- B. Steady State Voltammetry -- C. Cyclic Voltammetry -- D. Square Wave Voltammetry -- References