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Produktbild: Nonlinear Regression Modeling for Engineering Applications

Nonlinear Regression Modeling for Engineering Applications Modeling, Model Validation, and Enabling Design of Experiments

Aus der Reihe Wiley-ASME Press Series

163,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.10.2016

Verlag

John Wiley & Sons

Seitenzahl

400

Maße (L/B/H)

25,1/17,4/2,7 cm

Gewicht

751 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-59796-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.10.2016

Verlag

John Wiley & Sons

Seitenzahl

400

Maße (L/B/H)

25,1/17,4/2,7 cm

Gewicht

751 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-118-59796-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Nonlinear Regression Modeling for Engineering Applications
  • Series Preface xiii
     
    Preface xv
     
    Acknowledgments xxiii
     
    Nomenclature xxv
     
    Symbols xxxvii
     
    Part I INTRODUCTION
     
    1 Introductory Concepts 3
     
    1.1 Illustrative Example - Traditional Linear Least-Squares Regression 3
     
    1.2 How Models Are Used 7
     
    1.3 Nonlinear Regression 7
     
    1.4 Variable Types 8
     
    1.5 Simulation 12
     
    1.6 Issues 13
     
    1.7 Takeaway 15
     
    Exercises 15
     
    2 Model Types 16
     
    2.1 Model Terminology 16
     
    2.2 A Classification of Mathematical Model Types 17
     
    2.3 Steady-State and Dynamic Models 21
     
    2.3.1 Steady-State Models 22
     
    2.3.2 Dynamic Models (Time-Dependent, Transient) 24
     
    2.4 Pseudo-First Principles - Appropriated First Principles 26
     
    2.5 Pseudo-First Principles - Pseudo-Components 28
     
    2.6 Empirical Models with Theoretical Grounding 28
     
    2.6.1 Empirical Steady State 28
     
    2.6.2 Empirical Time-Dependent 30
     
    2.7 Empirical Models with No Theoretical Grounding 31
     
    2.8 Partitioned Models 31
     
    2.9 Empirical or Phenomenological? 32
     
    2.10 Ensemble Models 32
     
    2.11 Simulators 33
     
    2.12 Stochastic and Probabilistic Models 33
     
    2.13 Linearity 34
     
    2.14 Discrete or Continuous 36
     
    2.15 Constraints 36
     
    2.16 Model Design (Architecture, Functionality, Structure) 37
     
    2.17 Takeaway 37
     
    Exercises 37
     
    Part II PREPARATION FOR UNDERLYING SKILLS
     
    3 Propagation of Uncertainty 43
     
    3.1 Introduction 43
     
    3.2 Sources of Error and Uncertainty 44
     
    3.2.1 Estimation 45
     
    3.2.2 Discrimination 45
     
    3.2.3 Calibration Drift 45
     
    3.2.4 Accuracy 45
     
    3.2.5 Technique 46
     
    3.2.6 Constants and Data 46
     
    3.2.7 Noise 46
     
    3.2.8 Model and Equations 46
     
    3.2.9 Humans 47
     
    3.3 Significant Digits 47
     
    3.4 Rounding Off 48
     
    3.5 Estimating Uncertainty on Values 49
     
    3.5.1 Caution 50
     
    3.6 Propagation of Uncertainty - Overview - Two Types, Two Ways Each 51
     
    3.6.1 Maximum Uncertainty 51
     
    3.6.2 Probable Uncertainty 56
     
    3.6.3 Generality 58
     
    3.7 Which to Report? Maximum or Probable Uncertainty 59
     
    3.8 Bootstrapping 59
     
    3.9 Bias and Precision 61
     
    3.10 Takeaway 65
     
    Exercises 66
     
    4 Essential Probability and Statistics 67
     
    4.1 Variation and Its Role in Topics 67
     
    4.2 Histogram and Its PDF and CDF Views 67
     
    4.3 Constructing a Data-Based View of PDF and CDF 70
     
    4.4 Parameters that Characterize the Distribution 71
     
    4.5 Some Representative Distributions 72
     
    4.5.1 Gaussian Distribution 72
     
    4.5.2 Log-Normal Distribution 72
     
    4.5.3 Logistic Distribution 74
     
    4.5.4 Exponential Distribution 74
     
    4.5.5 Binomial Distribution 75
     
    4.6 Confidence Interval 76
     
    4.7 Central Limit Theorem 77
     
    4.8 Hypothesis and Testing 78
     
    4.9 Type I and Type II Errors, Alpha and Beta 80
     
    4.10 Essential Statistics for This Text 82
     
    4.10.1 t-Test for Bias 83
     
    4.10.2 Wilcoxon Signed Rank Test for Bias 83
     
    4.10.3 r-lag-1 Autocorrelation Test 84
     
    4.10.4 Runs Test 87
     
    4.10.5 Test for Steady State in a Noisy Signal 87
     
    4.10.6 Chi-Square Contingency Test 89
     
    4.10.7 Kolmogorov-Smirnov Distribution Test 89
     
    4.10.8 Test for Proportion 90
     
    4.10.9 F-Test for Equal Variance 90
     
    4.11 Takeaway 91
     
    Exerc