Introduction

Please select an option from the pull-down menu above.

Text data editor

Cut-and-paste text or edit data here

Convert commas to spaces (1,2,0,3 yields 1 2 0 3) Use comma as decimal separator (1,203 = 1.203) Remove commas from text (1,2,0,3 yields 1203)

Standard Unweighted Fitting: no weights required Weighted Fitting: each data point requires a weight

This data is provided as an example, cut and paste as needed to model your data. All lines of text that do not begin with a number are ignored. Weighted fitting requires an additional number to be used as a weight when fitting. The site does not calculate any weights, which are used as: error = weight * (predicted - actual) You must provide any weights you wish to use. X Y Z 3.017 2.175 0.320 2.822 2.624 0.629 2.632 2.839 0.950 2.287 3.030 1.574 2.207 3.057 1.725 2.048 3.098 2.035 1.963 3.115 2.204 1.784 3.144 2.570 1.712 3.153 2.721 2.972 2.106 0.313 2.719 2.542 0.643 2.495 2.721 0.956 2.070 2.878 1.597 1.969 2.899 1.758 1.768 2.929 2.088 1.677 2.939 2.240 1.479 2.957 2.583 1.387 2.963 2.744 2.843 1.984 0.315 2.485 2.320 0.639 2.163 2.444 0.954 1.687 2.525 1.459 1.408 2.547 1.775 1.279 2.554 1.927 1.016 2.564 2.243 0.742 2.568 2.581 0.607 2.571 2.753

Fitting Target

Here you can select the desired fitting target.

Lowest sum of squared absolute error Lowest sum of squared orthogonal distance Lowest sum of squared relative error Lowest sum of absolute value of absolute error Lowest sum of squared log[predicted/actual] Lowest sum of absolute value of relative error Lowest peak absolute value of absolute error Lowest peak absolute value of relative error Lowest Akaike Information Criterion Lowest Bayesian Information Criterion

Data labels for graphs

Here you can enter the X, Y and Z data labels to be printed on the report graphs.

Label for first independent data (X)

Label for second independent data (Y)

Label for dependent data (Z)

Graph scaling and lin/log control

Here you can choose the scaling of the data
plots and the 3D scatterplots.
Log plots require data values greater than zero.

X Scale (2D Only)		Y Scale (2D Only)		Z Scale (2D Only)
Autoscale +/- 1% Autoscale +/- 2.5% Autoscale +/- 5% Autoscale +/- 10% Autoscale +/- 25% Autoscale +/- 50% Autoscale +/- 100% Autoscale +/- 150% Autoscale +/- 200% Manual Scale Max X: Min X:   Linear Scaling Logarithmic Scaling		Autoscale +/- 1% Autoscale +/- 2.5% Autoscale +/- 5% Autoscale +/- 10% Autoscale +/- 25% Autoscale +/- 50% Autoscale +/- 100% Autoscale +/- 150% Autoscale +/- 200% Manual Scale Max Y: Min Y:   Linear Scaling Logarithmic Scaling		Autoscale +/- 1% Autoscale +/- 2.5% Autoscale +/- 5% Autoscale +/- 10% Autoscale +/- 25% Autoscale +/- 50% Autoscale +/- 100% Autoscale +/- 150% Autoscale +/- 200% Manual Scale Max Z: Min Z:   Linear Scaling Logarithmic Scaling

Animation and Graph size

Here you can choose the size of the graphs. Smaller size files load quickly,
with less resolution while large files show more resolution but load more slowly.

320 x 240 (fastest) 500 x 400 640 x 480 700 x 500 800 x 600 900 x 700 1024 x 768 1280 x 1024 1600 x 1200 (slowest) wide ratio 2400 x 400

Graph Size

320 x 240 (smallest) 500 x 400 640 x 480 700 x 500 800 x 600 900 x 700 (largest) No Animations

Animation Size

Scientific Notation

Here you can control scientific notation on the data plots
and the contour plots.

X Control		Y Control		Z Control
On Off Auto		On Off Auto		On Off Auto

Hold Coefficients Constant

Here you can enter values to hold coefficients constant during fitting.
Leave the entry field blank to let the coefficient fit normally.

Note that coefficient bounds are overridden by holding a coefficient constant.


z = amplitude_x * cosh(pi * (x - center_x) / width_x) + amplitude_y * tan(pi * (y - center_y) / width_y)
z = 1.0 / z + Offset

amplitude_x
center_x
width_x
amplitude_y
center_y
width_y
Offset

Estimated Initial Coefficients

Here you can enter values for initial parameter estimates.

Few users will need this section, as the site estimates initial
parameters using the Differential Evolution genetic algorithm
for both non-linear equations and non-Gaussian fitting targets.

z = amplitude_x * cosh(pi * (x - center_x) / width_x) + amplitude_y * tan(pi * (y - center_y) / width_y)
z = 1.0 / z + Offset

amplitude_x
center_x
width_x
amplitude_y
center_y
width_y
Offset

Coefficients Bounds

Here you can enter coefficient upper and lower bounds. Leave the
entry fields blank to let the coefficients fit without bounds.

Note that holding a coefficient constant overrides these bounds.


z = amplitude_x * cosh(pi * (x - center_x) / width_x) + amplitude_y * tan(pi * (y - center_y) / width_y)
z = 1.0 / z + Offset

	Upper     Bound	Lower     Bound
amplitude_x
center_x
width_x
amplitude_y
center_y
width_y
Offset

3D Viewing Angles

Here you can select and rotate the 3D scatter and surface plots.

0153045607590105120135150165170180195210225240255270285300315330345 Rotation view angle in degrees 051015202530354045505560657075808590 Vertical view angle in degrees

3D Data Point Size Options

Here you can select the data point size for 3D surface plots and 3D scatter plots.

3D Point Size

Tiny Small Medium Large Extra Large Auto