Linear And Nonlinear Regression With Matlab Fitting Curves And Surfaces To Data

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Linear and Nonlinear Regression With Matlab. Fitting Curves and Surfaces to Data

Author : Perez C.
Publisher :
ISBN 13 : 9781974615742
Total Pages : 338 pages
Book Rating : 4.6/5 (157 download)

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Book Synopsis Linear and Nonlinear Regression With Matlab. Fitting Curves and Surfaces to Data by : Perez C.

Download or read book Linear and Nonlinear Regression With Matlab. Fitting Curves and Surfaces to Data written by Perez C. and published by . This book was released on 2017-08-17 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB allows to work with linear and nonlinear regression models efficiently. It has tools that contemplate the phases of estimation, diagnosis and prediction.MATLAB Curve Fitting Toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting,interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methodsThis book develops the following topics:* "Curve Fitting" * "Surface Fitting" * "Spline Fitting" * "Parametric Fitting with Library Models" * "Polynomial Models" * "Exponential Models" * "Fourier Series Models"* "Gaussian Models"* "Power Series Models"* "Rational Models"* "Sum of Sines Models"* "Weibull Distribution Models"* "Least-Squares Fitting"* "Linear Least Squares" * "Weighted Least Squares" * "Robust Least Squares" * "Nonlinear Least Squares" * "Robust Fitting"* "Custom Linear and Nonlinear Regression" * "Nonparametric Fitting"* "Interpolation and Smoothing" * "Smoothing Splines"* "Filtering and Smoothing Data"* "Fit Postprocessing" * "Explore and Customize Plots" * "Remove Outliers" * "Select Validation Data" * "Evaluate a Curve Fit" * "Evaluate a Surface Fit"* "Compare Fits Programmatically" * "Evaluating Goodness of Fit"* "Residual Analysis" * "Confidence and Prediction Bounds"

CURVE and SURFACE FITTING with MATLAB. LINEAR and NONLINEAR REGRESSION

Author : A Ramirez
Publisher :
ISBN 13 : 9781082079726
Total Pages : 342 pages
Book Rating : 4.0/5 (797 download)

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Book Synopsis CURVE and SURFACE FITTING with MATLAB. LINEAR and NONLINEAR REGRESSION by : A Ramirez

Download or read book CURVE and SURFACE FITTING with MATLAB. LINEAR and NONLINEAR REGRESSION written by A Ramirez and published by . This book was released on 2019-07-22 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: You can fit curves and surfaces to data and view plots with the Curve Fitting app in MATLAB. Is possible: .Create, plot, and compare multiple fits.Use linear or nonlinear regression, interpolation, smoothing, and custom equations..View goodness-of-fit statistics, display confidence intervals and residuals, remove outliers and assess fit with validation data..Automatically generate code to fit and plot curves and surfaces, or export fits to the workspace for further analysis.Curve Fitting app makes it easy to plot and analyze fit at the command line. You can export individual fit to the workspace for further analysis, or you can generate MATLAB code to recreate all fit and plots in your session. By generating code, you can use your interactive curve fitting session to quickly assemble code for curve and surface fit and plots into useful programs.The Curve Fitting app allows convenient, interactive use of Curve Fitting Toolbox functions, without programming. You can, however, access Curve Fitting Toolbox functions directly, and write programs that combine curve fitting functions with MATLAB functions and functions from other toolboxes. This allows you to create a curve fitting environment that is precisely suited to your needs. Models and fit in the Curve Fitting app are managed internally as curve fitting objects. Objects are manipulated through a variety of functions called methods. You can create curve fitting objects, and apply curve fitting methods, outside of the Curve Fitting app

Curve Fitting With Matlab

Author : J. Braselton
Publisher : CreateSpace
ISBN 13 : 9781502333094
Total Pages : 200 pages
Book Rating : 4.3/5 (33 download)

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Book Synopsis Curve Fitting With Matlab by : J. Braselton

Download or read book Curve Fitting With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-10 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Linear and Nonlinear Regression Parametric Fitting Parametric Fitting with Library Models Selecting a Model Type Interactively Selecting Model Type Programmatically Using Normalize or Center and Scale Specifying Fit Options and Optimized Starting Points List of Library Models for Curve and Surface Fitting Use Library Models to Fit Data Library Model Types Model Names and Equations Polynomial Models About Polynomial Models Selecting a Polynomial Fit Interactively Selecting a Polynomial Fit at the Command Line Defining Polynomial Terms for Polynomial Surface Fits Exponential Models About Exponential Models Selecting an Exponential Fit Interactively Selecting an Exponential Fit at the Command Line Fourier Series About Fourier Series Models Selecting a Fourier Fit Interactively Selecting a Fourier Fit at the Command Line Gaussian Models About Gaussian Models Selecting a Gaussian Fit Interactively Selecting a Gaussian Fit at the Command Line Power Series About Power Series Models Selecting a Power Fit Interactively Selecting a Power Fit at the Command Line Rational Polynomials About Rational Models Selecting a Rational Fit Interactively Selecting a Rational Fit at the Command Line Sum of Sines Models About Sum of Sines Models Selecting a Sum of Sine Fit Interactively Selecting a Sum of Sine Fit at the Command Line Weibull Distributions About Weibull Distribution Models Selecting a Weibull Fit Interactively Selecting a Weibull Fit at the Command Line Least-Squares Fitting Introduction Error Distributions Linear Least Squares Weighted Least Squares Robust Least Squares Nonlinear Least Squares Custom Linear and Nonlinear Regression Interpolation and Smoothing Nonparametric Fitting Interpolants Interpolation Methods Selecting an Interpolant Fit Interactively Selecting an Interpolant Fit at the Command Line Smoothing Splines About Smoothing Splines Selecting a Smoothing Spline Fit Interactively Selecting a Smoothing Spline Fit at the Command Line Lowess Smoothing About Lowess Smoothing Selecting a Lowess Fit Interactively Selecting a Lowess Fit at the Command Line Fitting Automotive Fuel Efficiency Surfaces 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 Using Zoom, Pan, Data Cursor, and Outlier Exclusion Customizing the Fit Display Print to MATLAB Figures Removing Outliers Selecting Validation Data Generating Code and Exporting Fits to the Workspace Generating Code from the Curve Fitting Tool Exporting a Fit to the Workspace Evaluating Goodness of Fit How to Evaluate Goodness of Fit Goodness-of-Fit Statistics Residual Analysis Plotting and Analysing Residuals Confidence and Prediction Bounds About Confidence and Prediction Bounds Confidence Bounds on Coefficients Prediction Bounds on Fits Differentiating and Integrating a Fit Surface Fitting Objects and Methods

Curve Fitting with MATLAB. Linear and Non Linear Regression. Interpolation

Author : Braselton J.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781534802704
Total Pages : 200 pages
Book Rating : 4.8/5 (27 download)

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Book Synopsis Curve Fitting with MATLAB. Linear and Non Linear Regression. Interpolation by : Braselton J.

Download or read book Curve Fitting with MATLAB. Linear and Non Linear Regression. Interpolation written by Braselton J. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-21 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox(tm) provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.

Econometrics With Matlab

Author : A. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979559614
Total Pages : 336 pages
Book Rating : 4.5/5 (596 download)

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Book Synopsis Econometrics With Matlab by : A. Smith

Download or read book Econometrics With Matlab written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-08 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important content is the following: - Curve Fitting app for curve and surface fitting - Linear and nonlinear regression with custom equations - Library of regression models with optimized starting points and solver parameters - Interpolation methods, including B-splines, thin plate splines, and tensor-productsplines - Smoothing techniques, including smoothing splines, localized regression, Savitzky-Golay filters, and moving averages - Preprocessing routines, including outlier removal and sectioning, scaling, andweighting data - Post-processing routines, including interpolation, extrapolation, confidence intervals, integrals and derivatives

CURVE and SURFACE FITTING with MATLAB. INTERPOLATION, SMOOTHING and SPLINE FITTING

Author : A Ramirez
Publisher :
ISBN 13 : 9781082263231
Total Pages : 242 pages
Book Rating : 4.2/5 (632 download)

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Book Synopsis CURVE and SURFACE FITTING with MATLAB. INTERPOLATION, SMOOTHING and SPLINE FITTING by : A Ramirez

Download or read book CURVE and SURFACE FITTING with MATLAB. INTERPOLATION, SMOOTHING and SPLINE FITTING written by A Ramirez and published by . This book was released on 2019-07-24 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Curve Fitting Toolbox software supports these nonparametric fitting methods: -"Interpolation Methods" - Estimate values that lie between known data points.-"Smoothing Splines" - Create a smooth curve through the data. You adjust the level of smoothness by varying a parameter that changes the curve from a least-squares straight-line approximation to a cubic spline interpolant.-"Lowess Smoothing" - Create a smooth surface through the data using locally weighted linear regression to smooth data.Interpolation is a process for estimating values that lie between known data points. There are several interpolation methods: - Linear: Linear interpolation. This method fit a different linear polynomial between each pair of data points for curves, or between sets of three points for surfaces.- Nearest neighbor: Nearest neighbor interpolation. This method sets the value of an interpolated point to the value of the nearest data point. Therefore, this method does not generate any new data points.- Cubic spline: Cubic spline interpolation. This method fit a different cubic polynomial between each pair of data points for curves, or between sets of three points for surfaces.After fitting data with one or more models, you should evaluate the goodness of fit A visual examination of the fitte curve displayed in Curve Fitting app should be your firs step. Beyond that, the toolbox provides these methods to assess goodness of fi for both linear and nonlinear parametric fits-"Goodness-of-Fit Statistics" -"Residual Analysis" -"Confidence and Prediction Bounds" The Curve Fitting Toolbox spline functions are a collection of tools for creating, viewing, and analyzing spline approximations of data. Splines are smooth piecewise polynomials that can be used to represent functions over large intervals, where it would be impractical to use a single approximating polynomial. The spline functionality includes a graphical user interface (GUI) that provides easy access to functions for creating, visualizing, and manipulating splines. The toolbox also contains functions that enable you to evaluate, plot, combine, differentiate and integrate splines. Because all toolbox functions are implemented in the open MATLAB language, you can inspect the algorithms, modify the source code, and create your own custom functions. Key spline features: -GUIs that let you create, view, and manipulate splines and manage and compare spline approximations-Functions for advanced spline operations, including differentiation integration, break/knot manipulation, and optimal knot placement-Support for piecewise polynomial form (ppform) and basis form (B-form) splines-Support for tensor-product splines and rational splines (including NURBS)- Shape-preserving: Piecewise cubic Hermite interpolation (PCHIP). This method preserves monotonicity and the shape of the data. For curves only.- Biharmonic (v4): MATLAB 4 grid data method. For surfaces only.- Thin-plate spline: Thin-plate spline interpolation. This method fit smooth surfaces that also extrapolate well. For surfaces only.If your data is noisy, you might want to fit it using a smoothing spline. Alternatively, you can use one of the smoothing methods. The smoothing spline s is constructed for the specified smoothing parameter p and the specified weights wi.

Fitting Models to Biological Data Using Linear and Nonlinear Regression

Author : Harvey Motulsky
Publisher : Oxford University Press
ISBN 13 : 0198038348
Total Pages : 352 pages
Book Rating : 4.1/5 (98 download)

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Book Synopsis Fitting Models to Biological Data Using Linear and Nonlinear Regression by : Harvey Motulsky

Download or read book Fitting Models to Biological Data Using Linear and Nonlinear Regression written by Harvey Motulsky and published by Oxford University Press. This book was released on 2004-05-27 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

CURVE and SURFACE FITTING with MATLAB. FUNCTIONS and EXAMPLES

Author : A Ramirez
Publisher :
ISBN 13 : 9781082453557
Total Pages : 306 pages
Book Rating : 4.4/5 (535 download)

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Book Synopsis CURVE and SURFACE FITTING with MATLAB. FUNCTIONS and EXAMPLES by : A Ramirez

Download or read book CURVE and SURFACE FITTING with MATLAB. FUNCTIONS and EXAMPLES written by A Ramirez and published by . This book was released on 2019-07-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.This book delves into the curve and surface fitting functions presented its complete syntax and completing them with examples.

Curve and Surface Fitting With Matlab

Author : J. Braselton
Publisher : CreateSpace
ISBN 13 : 9781502336071
Total Pages : 70 pages
Book Rating : 4.3/5 (36 download)

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Book Synopsis Curve and Surface Fitting With Matlab by : J. Braselton

Download or read book Curve and Surface Fitting With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-11 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Interactive Curve and Surface Fitting Introducing the Curve Fitting Tool Fitting a Curve Fitting a Surface Model Types for Curves and Surfaces Interactive Fit Comparison Refining Your Fit Creating Multiple Fits Duplicating a Fit Deleting a Fit Displaying Multiple Fits Simultaneously Using the Statistics in the Table of Fits Generating MATLAB Code and Exporting Fits Interactive Code Generation and Programmatic Fitting Curve Fitting to Census Data Interactive Curve Fitting Workflow Loading Data and Creating Fits Determining the Best Fit Analyzing Your Best Fit in the Workspace Saving Your Work Surface Fitting to Franke Data Programmatic Curve and Surface Fitting Curve and Surface Fitting Objects and Methods Curve Fitting Objects Curve Fitting Methods Surface Fitting Objects and Methods

Curve and Surface Fitting with MATLAB

Author : J. Braselton
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781534835382
Total Pages : 160 pages
Book Rating : 4.8/5 (353 download)

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Book Synopsis Curve and Surface Fitting with MATLAB by : J. Braselton

Download or read book Curve and Surface Fitting with MATLAB written by J. Braselton and published by Createspace Independent Publishing Platform. This book was released on 2016-06-22 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox(tm) provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox(tm) software allows you to work in two different environments:An interactive environment, with the Curve Fitting app and the Spline ToolA programmatic environment that allows you to write object-oriented MATLAB(r) code using curve and surface fitting methods

Fitting Curves and Sourfaces Using Matlab

Author : Perez C.
Publisher :
ISBN 13 : 9781974616077
Total Pages : 236 pages
Book Rating : 4.6/5 (16 download)

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Book Synopsis Fitting Curves and Sourfaces Using Matlab by : Perez C.

Download or read book Fitting Curves and Sourfaces Using Matlab written by Perez C. and published by . This book was released on 2017-08-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Curve Fitting Toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting,interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methodsThe more important features of this toolbox ar de next:* Curve Fitting app for curve and surface fitting* Linear and nonlinear regression with custom equations* Library of regression models with optimized starting points and solver parameters* Interpolation methods, including B-splines, thin plate splines, and tensor-productsplines* Smoothing techniques, including smoothing splines, localized regression, Savitzky-Golay filters, and moving averages* Preprocessing routines, including outlier removal and sectioning, scaling, and weighting data* Post-processing routines, including interpolation, extrapolation, confidence intervals, integrals and derivatives This book develops the following topics:* "Interpolation and Smoothing" * "Nonparametric Fitting" * "Interpolation Methods" * "Smoothing Splines" * "Lowess Smoothing" * "Filtering and Smoothing Data"* "Fit Postprocessing" * "Explore and Customize Plots" * "Remove Outliers" * "Select Validation Data" * "Evaluate a Curve Fit" * "Evaluate a Surface Fit"* "Compare Fits Programmatically" * "Evaluating Goodness of Fit"* "Residual Analysis" * "Confidence and Prediction Bounds"* "Differentiating and Integrating a Fit" * "Spline Fitting" * "Curve Fitting Toolbox Splines and MATLAB Splines" * "Cubic Spline Interpolation" * "Fitting Values at N-D Grid with Tensor-Product Splines" * "Postprocessing Splines"* "Types of Splines: ppform and B-form" * "B-Splines and Smoothing Splines"* "Multivariate and Rational Splines" * "Multivariate Tensor Product Splines"* "NURBS and Other Rational Splines" * "Least-Squares Approximation by Natural Cubic Splines" * "Solving A Nonlinear ODE" * "Construction of the Chebyshev Spline" * "Approximation by Tensor Product Splines"

Curve and Surface Fitting Functions With Matlab

Author : J. Braselton
Publisher : CreateSpace
ISBN 13 : 9781502332707
Total Pages : 282 pages
Book Rating : 4.3/5 (327 download)

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Book Synopsis Curve and Surface Fitting Functions With Matlab by : J. Braselton

Download or read book Curve and Surface Fitting Functions With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-10 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. This book explains through examples all Curve Fitting Toolbox functions

Curve and Surface Fitting Functions with MATLAB

Author : J. Braselton
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781534839458
Total Pages : 266 pages
Book Rating : 4.8/5 (394 download)

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Book Synopsis Curve and Surface Fitting Functions with MATLAB by : J. Braselton

Download or read book Curve and Surface Fitting Functions with MATLAB written by J. Braselton and published by Createspace Independent Publishing Platform. This book was released on 2016-06-22 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the syntax of functions of Curve Fitting Toolbox(tm). This package provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.

Data Analysis with MATLAB

Author : James Braselton
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781523453313
Total Pages : 158 pages
Book Rating : 4.4/5 (533 download)

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Book Synopsis Data Analysis with MATLAB by : James Braselton

Download or read book Data Analysis with MATLAB written by James Braselton and published by Createspace Independent Publishing Platform. This book was released on 2016-01-18 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contents of this book is focused on basic data analysis with MATLAB. Initially the import and export of data, key tasks in any kind of analysis is studied. Then numerical and graphical data exploratory analysis is presented. The next block of content is focused on descriptive statistics and correlation analysis, covariance and regression. These topics are expanded to the study of model simple and multiple linear regression and Curve Fitting. Polynomial regression and nonlinear regression is also studied. Finally an important piece of content is devoted to the time series analysis in interactive mode and command mode. The following topics are developed: Importing and Exporting Data Plotting Data Missing Data Inconsistent Data Filtering Data Filter Function Moving Average Filter Discrete Filter Detrending Data Removing Linear Trends from Data Differencing Data Descriptive Statistics Functions for Calculating Descriptive Statistics Interactive Data Exploration Interacting with MATLAB Data Graphs Marking Up Graphs with Data Brushing Effects of Brushing on Data Making Graphs Responsive with Data Linking How to Link Plots How Linked Plots Behave Linking vs. Refreshing Plots Using Linked Plot Controls Interacting with Graphed Data Data Brushing with the Variables Editor Using Data Tips to Explore Graphs Regression Analysis Linear Correlation Covariance Correlation Coefficients Linear Regression Residuals and Goodness of Fit Fitting Data with Curve Fitting Toolbox Functions Interactive Fitting The Basic Fitting GUI Preparing for Basic Fitting Opening the Basic Fitting GUI Programmatic Fitting MATLAB Functions for Polynomial Models Linear Model with Nonpolynomial Terms Multiple Regression Time Series Analysis Time Series Objects Time Series Data Sample Time Series Objects and Methods Time Series Constructor Time Series Collection Constructor Time Series Tools Importing and Exporting Data Plotting Time Series Selecting Data for Analysis Editing Data, Time, Attributes, and Events Processing and Manipulating Time Series

Spline Fitting With Matlab

Author : J. Braselton
Publisher : CreateSpace
ISBN 13 : 9781502332462
Total Pages : 114 pages
Book Rating : 4.3/5 (324 download)

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Book Synopsis Spline Fitting With Matlab by : J. Braselton

Download or read book Spline Fitting With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-10 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Interactive Spline Fitting Programmatic Spline Fitting Curve Fitting Toolbox Splines MATLAB Splines Expected Background Vector Data Type Support Spline Function Naming Conventions Arguments for Curve Fitting Toolbox Spline Functions Cubic Spline Interpolation Cubic Spline Interpolant of Smooth Data Periodic Data Other End Conditions General Spline Interpolation Knot Choices Smoothing Least Squares Vector-Valued Functions Fitting Values at N-D Grid with Tensor-Product Splines Fitting Values at Scattered 2-D Sites with Thin-Plate Smoothing Splines Postprocessing Splines B-Splines and Smoothing Splines Multivariate and Rational SplinesLeast-Squares Approximation by Natural Cubic Splines Solving A Nonlinear ODE Construction of the Chebyshev Spline Approximation by Tensor Product Splines

Fitting Curves and Sourfaces Using Matlab Functions

Author : Perez C.
Publisher :
ISBN 13 : 9781974616572
Total Pages : 300 pages
Book Rating : 4.6/5 (165 download)

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Book Synopsis Fitting Curves and Sourfaces Using Matlab Functions by : Perez C.

Download or read book Fitting Curves and Sourfaces Using Matlab Functions written by Perez C. and published by . This book was released on 2017-08-17 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methods and using curve fitting functions.MATLAB Curve Fitting Functions lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis and provide optimized solver parameters and starting conditions to improve the quality of your fits. The functions also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.

Econometrics With Matlab

Author : A. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979562706
Total Pages : 162 pages
Book Rating : 4.5/5 (627 download)

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Book Synopsis Econometrics With Matlab by : A. Smith

Download or read book Econometrics With Matlab written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-08 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important content is the following: -Nonparametric Fitting -Interpolation Methods -Selecting an Interpolant Fit -Smoothing Splines -Lowess Smoothing -Fit Smooth Surfaces To Investigate Fuel Efficiency -Filtering and Smoothing Data -Introducing Spline Fitting -Curve Fitting Toolbox Splines and MATLAB Splines -Cubic Spline Interpolation -Vector-Valued Functions -Fitting Values at N-D Grid with Tensor-Product -Splines -Fitting Values at Scattered 2-D Sites with Thin-Plate -Smoothing Splines -Postprocessing Splines -Types of Splines: ppform and B-form -B-Splines and Smoothing Splines -Multivariate and Rational Splines -The ppform -Constructing and Working with ppform Splines -The B-form -B-form and B-Splines -B-Spline Knot Multiplicity -Choice of Knots for B-form -Constructing and Working with B-form Splines -Multivariate Tensor Product Splines -B-form of Tensor Product Splines -Construction With Gridded Data -ppform of Tensor Product Splines -NURBS and Other Rational Splines -Constructing and Working with Rational Splines -Constructing and Working with stform Splines -Least-Squares Approximation by Natural Cubic Splines -Solving A Nonlinear ODE -Construction of the Chebyshev Spline -Approximation by Tensor Product Splines