This book focuses on in-depth treatment of predictive analytic models. The most important content is as follows: Linear and Nonlinear Regression Parametric Fitting Selecting a Model Type Interactively Selecting Model Type Programmatically Use Library Models to Fit Data Library Model Types Polynomial Models Selecting a Polynomial Fit Interactively Defining Polynomial Terms for Polynomial Surface Fits Exponential Models Selecting an Exponential Fit Interactively Selecting an Exponential Fit at the Command Line Fourier Series Selecting a Fourier Fit Interactively Selecting a Fourier Fit at the Command Line Gaussian Models Selecting a Gaussian Fit Interactively Selecting a Gaussian Fit at the Command Line Power Series Selecting a Power Fit Interactively Selecting a Power Fit at the Command Line Rational Polynomials Selecting a Rational Fit Interactively Selecting a Rational Fit at the Command Line Rational Fit Sum of Sines Models Selecting a Weibull Fit Interactively Selecting a Weibull Fit at the Command Line Least-Squares Fitting Linear Least Squares Weighted Least Squares Robust Least Squares Nonlinear Least Squares Custom Linear and Nonlinear Regression Interpolation and Smoothing Nonparametric Fitting Selecting a Smoothing Spline Fit Interactively Selecting a Smoothing Spline Fit at the Command Line Lowess Smoothing Selecting a Lowess Fit Interactively Selecting a Lowess Fit at the Command Line Filtering and Smoothing Data About Data Smoothing and Filtering Moving Average Filtering Savitzky-Golay Filtering Local Regression Smoothing Fit Postprocessing Exploring and Customizing Plots Displaying Fit and Residual Plots Viewing Surface Plots and Contour Plots Removing Outliers Selecting Validation Data Generating Code and Exporting Fits to the Workspace Evaluating Goodness of Fit Residual Analysis Confidence Bounds on Coefficients Prediction Bounds on Fits General Global Models Polynomials and Polynomial Splines Truncated Power Series Growth Models Linear Models Average Fit Multiple Models Transient Models Covariance Modeling Correlation Models Transforms Boundary Model Setup Combining Best Boundary Models Radial Basis Function Hybrid RBF Interpolating RBF Multiple Linear Models Free Knot Spline Neural Network Datum Models Compare and Select Best Models Plots and Statistics for Comparing Models Determining the Best Fit Validation Trends Using Information Criteria to Compare Models Calculate and Compare MLE Two-Stage Response Model Stepwise Regression Automatic Stepwise Box-Cox Transformation Two-Stage Models for Engines Local Covariance Modeling Response Features Global Models Two-Stage Models Global Model Selection Linear Regression Use Statistical Models for Plant Modeling and Optimization Use Statistical Models for Hardware-in-the-Loop Testing Evaluate Response Models and PEV Evaluate Confidence Intervals Evaluate Boundary Models in the Workspace Radial Basis Functions Radial Basis Functions for Model Building Hybrid Radial Basis Functions