Nonlinear computer modeling of chemical and biochemical data Book

Part I: General Introduction to Regression Analysis. Introduction to Nonlinear Modeling of Data: What is Nonlinear Modeling? Objectives of this Book. Anayzing Data with Regression Analysis: Linear Models. Nonlinear Regression Analysis. Sources of Mathematics Software Capable of Linear and Nonlinear Regression. Building Models for Experimental Data: Sources of Data and Background Contributions. Examples of Model Types. Finding the Best Models. Correlation Between Parameters and Other Convergence Problems: Correlations and How to Minimize Them. Avoiding Pitfalls in Convergence. Part II: Selected Applications. Titrations: Introduction. Macromolecular Equilibria and Kinetics: Linked Thermodynamic Models: The Concept of Linked Functions. Applications of Thermodynamic Linkage. Secondary Structure of Proteins by Infrared Spectroscopy: Introduction. Analysis of Spectra--Examples. Nuclear Magnetic Resonance Relaxation: Fundamentals of NMR Relaxation. Applications from NMR in Solution. Applications from NMR in the Solid State. Small-Angle X-Ray Scattering (SAXS) of Proteins: Theoretical Considerations. Applications. Ultracentrifugation of Macromolecules: Sedimentation. Voltammetric Methods: General Characteristics of Voltammetry. Steady State Voltammetry. Cyclic Voltammetry. Square Wave Voltammetry.Chronocoulometry: Basic Principles. Estimation of Diffusion Coefficients. Surface Concentrations of Adsorbates from Double Potential Steps. Rate Constant for Reaction of a Product of an Electrochemical Reaction. Automated Resolution of Multiexponential Decay Data: Considerations for Analyses of Overlapped Signals. Automated Analysis of Data with an Unknown Number of Exponentials. Chromatography and Multichannel Detection Methods: Overlapped Chromatographic Peaks with Single-Channel Detection. Multichannel Detection. Appendix. Subject Index.