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

By
R. Russell Rhinehart
R. Russell Rhinehart
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ISBN:
9781118597965
No. of Pages:
400
Publisher:
ASME-Wiley
Publication date:
2016

Most engineering or science models provide a continuous-valued, deterministic response. These could either represent steady-state or transient phenomena. In contrast, models could predict a classification (nominal, class, text, or string variable) or a rank, but still a deterministic value. Alternately, Monte Carlo simulations predict a stochastic outcome, a range of possibilities, not a definitive value. There are diverse options to what you may be seeking to best fit, and you need to understand the application to choose the regression target.

7.1
Introduction
7.2
Experimental and Measurement Uncertainty – Static and Continuous Valued
7.3
Likelihood
7.4
Maximum Likelihood
7.5
Estimating σx and σy 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
7.13
Model Prediction with Classification Models
7.14
Model Prediction with Rank Models
7.15
Probabilistic Models
7.16
Stochastic Models
7.17
Takeaway
Exercises
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