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[null,"1.","Record Nr.",null,"UNINA9910823773703321"]
[null,null,"Autore",null,"Rhinehart R. Russell <1946->"]
[null,null,"Titolo",null,"Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / / R. Russell Rhinehart"]
[null,null,"Pubbl/distr/stampa",null,"Chichester, England : , : Wiley : , : ASME Press, , 2016"]
[{"$nulls":4},"©2016"]
[null,null,"ISBN",null,"1-5231-5487-X"]
[{"$nulls":4},"1-118-59795-8"]
[{"$nulls":4},"1-118-59793-1"]
[{"$nulls":4},"1-118-59797-4"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (403 p.)"]
[null,null,"Collana",null,"Wiley-ASME Press Series"]
[null,null,"Disciplina",null,"620.001/519536"]
[null,null,"Soggetti",null,"Regression analysis - Mathematical models"]
[{"$nulls":4},"Engineering - Mathematical models"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright; Contents; Series Preface; Preface; Acknowledgments; Nomenclature; Symbols; Part I Introduction; Chapter 1 Introductory Concepts; 1.1 Illustrative Example-Traditional Linear Least-Squares Regression; 1.2 How Models Are Used; 1.3 Nonlinear Regression; 1.4 Variable Types; 1.5 Simulation; 1.6 Issues; 1.7 Takeaway; Exercises; Chapter 2 Model Types; 2.1 Model Terminology; 2.2 A Classification of Mathematical Model Types; 2.3 Steady-State and Dynamic Models; 2.4 Pseudo-First Principles-Appropriated First Principles; 2.5 Pseudo-First Principles-Pseudo-Components"]
[{"$nulls":4},"2.6 Empirical Models with Theoretical Grounding2.7 Empirical Models with No Theoretical Grounding; 2.8 Partitioned Models; 2.9 Empirical or Phenomenological?; 2.10 Ensemble Models; 2.11 Simulators; 2.12 Stochastic and Probabilistic Models; 2.13 Linearity; 2.14 Discrete or Continuous; 2.15 Constraints; 2.16 Model Design (Architecture, Functionality, Structure); 2.17 Takeaway; Exercises; Part II Preparation for Underlying Skills; Chapter 3 Propagation of Uncertainty; 3.1 Introduction; 3.2 Sources of Error and Uncertainty; 3.3 Significant Digits; 3.4 Rounding Off"]
[{"$nulls":4},"3.5 Estimating Uncertainty on Values3.6 Propagation of Uncertainty-Overview-Two Types, Two Ways Each; 3.7 Which to Report? Maximum "]
[{"$nulls":4},"or Probable Uncertainty; 3.8 Bootstrapping; 3.9 Bias and Precision; 3.10 Takeaway; Exercises; Chapter 4 Essential Probability and Statistics; 4.1 Variation and Its Role in Topics; 4.2 Histogram and Its PDF and CDF Views; 4.3 Constructing a Data-Based View of PDF and CDF; 4.4 Parameters that Characterize the Distribution; 4.5 Some Representative Distributions; 4.6 Confidence Interval; 4.7 Central Limit Theorem; 4.8 Hypothesis and Testing"]
[{"$nulls":4},"4.9 Type I and Type II Errors, Alpha and Beta4.10 Essential Statistics for This Text; 4.11 Takeaway; Exercises; Chapter 5 Simulation; 5.1 Introduction; 5.2 Three Sources of Deviation: Measurement, Inputs, Coefficients; 5.3 Two Types of Perturbations: Noise (Independent) and Drifts (Persistence); 5.4 Two Types of Influence: Additive and Scaled with Level; 5.5 Using the Inverse CDF to Generate n and u from UID(0, 1); 5.6 Takeaway; Exercises; Chapter 6 Steady and Transient State Detection; 6.1 Introduction; 6.2 Method; 6.3 Applications; 6.4 Takeaway; Exercises"]
[{"$nulls":4},"Part III Regression, Validation, DesignChapter 7 Regression Target - Objective Function; 7.1 Introduction; 7.2 Experimental and Measurement Uncertainty-Static and Continuous Valued; 7.3 Likelihood; 7.4 Maximum Likelihood; 7.5 Estimating   x and   y Values; 7.6 Vertical SSD-A Limiting Consideration of Variability Only in the Response Measurement; 7.7 r-Square as a Measure of Fit; 7.8 Normal, Total, or Perpendicular SSD; 7.9 Akaho's Method; 7.10 Using a Model Inverse for Regression; 7.11 Choosing the Dependent Variable; 7.12 Model Prediction with Dynamic Models"]
[{"$nulls":4},"7.13 Model Prediction with Classification Models"]