MATLAB Examples

Fit and compare polynomials up to sixth degree using Curve Fitting Toolbox, fitting some census data. It also shows how to fit a single-term exponential equation and compare this to the

Work with a curve fit.

Work with a surface fit.

Find the first and second derivatives of a fit, and the integral of the fit, at the predictor values.

Remove outliers when curve fitting programmatically, using the 'Exclude' name/value pair argument with the fit or fitoptions functions. You can plot excluded data by supplying an Exclude

Use the fit function to fit linear interpolant models to data.

Fit an exponential model to data using the fit function.

Use the fit function to fit polynomials to data. The steps fit and plot polynomial curves and a surface, specify fit options, return goodness of fit statistics, calculate predictions, and

Use the fit function to fit a Fourier model to data.

Compare the effects of excluding outliers and robust fitting. The example shows how to exclude outliers at an arbitrary distance greater than 1.5 standard deviations from the model. The

Fit a custom equation to census data, specifying bounds, coefficients, and a problem-dependent parameter.

Use Curve Fitting Toolbox™ to fit response surfaces to some anesthesia data to analyze drug interaction effects. Response surface models provide a good method for understanding the

Fit polynomials up to sixth degree to some census data using Curve Fitting Toolbox™. It also shows how to fit a single-term exponential equation and compare this to the polynomial models.

Use the fit function to fit power series models to data.

Use the fit function to fit a Gaussian model to data.

Use Curve Fitting Toolbox™ to fit a response surface to some automotive data to investigate fuel efficiency.

Use the fit function to fit a smoothing spline model to data.

Use the fit function to fit a Lowess model to data.

Construct and work with the ppform of a spline in Curve Fitting Toolbox™.

Use the spmak, spcrv, cscvn and rscvn commands from Curve Fitting Toolbox™ to construct spline curves in the plane. This includes plotting tangents and computing the area enclosed by a

Use the cscvn command from Curve Fitting Toolbox™ to construct cubic spline curves in two and three dimensions.

Use the spline commands in Curve Fitting Toolbox™ to fit tensor product splines to bivariate gridded data.

Use spline commands from Curve Fitting Toolbox™ solve a nonlinear ordinary differential equation (ODE).

Construct splines in various ways using the spline functions in Curve Fitting Toolbox™.

Use the csaps and spaps commands from Curve Fitting Toolbox™ to construct cubic smoothing splines.

Use commands from Curve Fitting Toolbox™ to fit a spline to titanium test data with manual and automatic selection of knots.

Use commands from Curve Fitting Toolbox™ to construct a Chebyshev spline.

Use the csapi and csape commands from Curve Fitting Toolbox™ to construct cubic spline interpolants.

Use spline commands from Curve Fitting Toolbox™ to smooth a histogram.

Construct and work with the B-form of a spline in Curve Fitting Toolbox™.

Select and optimize knots using the optknt and newknt commands from Curve Fitting Toolbox™.

Small, round blue-cell tumors (SRBCTs) belong to four distinct diagnostic categories. The categories have widely differing prognoses and treatment options, making it extremely

Demonstrates fitting a non-linear regression tree model to hourly day-ahead electricity prices in the New England pool region. The log electricity prices are modeled with two additive

Copyright 2016 The MathWorks, Inc.Published with MATLAB® R2016a

Demo file from August 7, 2007 webinar titled Data Analysis with Statistics Toolbox and Curve Fitting Toolbox. View the recorded webinar:

This demo is an example of performing data mining on historical fuel economy data. We have data from various cars built from year 2000 up to 2012.

Goal - Produce a reliable med term forecasting model for Energy Demand

The Natural Gas Price model, Temperature model and Electricity Price hybrid model are jointly simulated to create market scenarios. Then, given a set of plant parameters and constraints a

Demonstrates fitting a non-linear temperature model to hourly dry bulb temperatures recorded in the New England region. The temperature series is modeled as a sum of two compoments, a

Demonstrates calibrating an Ornstein-Uhlenbeck mean reverting stochastic model from historical data of natural gas prices. The model is then used to simulate the spot prices into the

This demo shows how to price a GMWB rider. Yi Wang, MathWorks, 2010

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The aim of this demo is to characterize the "complete spectrum of interaction" between opiods and hypnotics, using propofol and remifentanil as drug class prototypes [1]. 4 different

In this example, we look at our efforts to characterize a device under test. We have this "black box" that we need to better understand. We might just want to know some of it's characteristics,

緯度

This demo showcases visualization and analysis (heavy statistics) for forecasting energy usage based on historical data. We have access to hour-by-hour utility usage for the month of

In this demo, we use regression trees to predict the fuel economy of vehicles.

This demo uses MATLAB, the Statistics Toolbox, the Curve Fitting Toolbox, and the Optimization Toolbox to improve the design of an engine cooling fan using Design for Six Sigma Techniques.

Government intelligence agencies need to continually analyze thousands of images of enemy territory. They are always looking for change - for instance, has the enemy relocated any of its

Copyright 2018 The MathWorks, Inc

Análise dos dados de um sistema de pêndulo de mola. Os dados foram obtidos no site:

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