Product Design, Manufacturing & Innovation Resources

MyCurveFit

Data Analysis & Visualization

Free
No Download/Install required
No registration

MyCurveFit fits mathematical models to measurement data directly in the browser. Data pairs are pasted or typed into a table, a model is selected or defined, and the tool returns the fitted equation, the parameter values and a plot with the curve over the points.

Standard models such as linear, polynomial, exponential, power, logarithmic and sigmoidal curves are available, along with user-defined equations. The result includes goodness of fit measures such as R squared, and values can be predicted for new x values from the fitted curve.

The tool suits calibration curves, dose response data, decay and growth measurements and quick empirical correlations.

For statistical reporting with confidence intervals or weighting, a full package such as R or Python with SciPy is the better choice.

Disciplines: Scientific research, Pharmacology, Chemistry, Engineering, Statistics, Calibration.

Free alternative to OriginPro, GraphPad Prism, CurveExpert, MATLAB Curve Fitting Toolbox.

MyCurveFit compared with OriginPro, GraphPad Prism, CurveExpert and MATLAB Curve Fitting Toolbox
MyCurveFit	OriginPro	GraphPad Prism	CurveExpert	MATLAB Curve Fitting Toolbox
Model scope: Fits data with several fit methods or with a user-defined equation, and the result can be used to make predictions. Models are entered on the web page and not scripted.	Model scope: Covers linear and nonlinear fitting, and the Pro level adds fitting with X and Y errors, orthogonal distance regression, global fitting of several curves and ranking of all functions in a category.	Model scope: Ships with over 100 prebuilt nonlinear models, allows custom models, and lets parameters be constrained.	Model scope: Offers over 60 built-in nonlinear models with automatic initial guesses, can rank the models against the data, and accepts custom models written as Python functions.	Model scope: Provides a library of linear and nonlinear models such as polynomial, exponential, Gaussian, power, rational and Weibull, custom equations, and spline and smoothing methods for curves and surfaces.
	Statistical output: Fitting Comparison compares two models on one dataset or two datasets on one model, which supports model selection beyond a visual check.	Statistical output: Identifies outliers and assesses statistical significance, and gives step-by-step guidance on analysis choices and assumptions.	Statistical output: Produces HTML reports with parameter uncertainties, covariance matrices, residual plots, F-tests and Akaike information criterion, offers six weighting schemes, and was validated against the NIST Statistical Reference Datasets.	Statistical output: Confidence intervals can be estimated in post-processing, and the documentation also covers confidence and prediction intervals.
Platform and delivery: Runs in the browser and needs JavaScript. Results can be exported to Excel, PDF, Word and PowerPoint, and shared online.		Platform and delivery: Commercial desktop software for Windows and Mac, with a free trial and a cloud feature for sharing projects.	Platform and delivery: Desktop application for Windows, Mac and Linux, and saved files can move between the systems.	Platform and delivery: Requires MATLAB. The Curve Fitter app works interactively and can generate MATLAB code, and a free trial is offered with pricing through sales.
Intended use: Quick fitting of a data table and sharing of the result, without installing software.		Intended use: Aimed at researchers and scientists, with use cases such as dose-response modelling, pharmacokinetics, population growth or decay, enzyme inhibition and saturation binding.	Intended use: General curve fitting and data analysis with publication-quality 2D and 3D graphs, including multivariate regression with no limit on independent variables.	Intended use: Fitting of curves and surfaces to data inside the MATLAB environment, so fits can be combined with other MATLAB code.

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