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 5.357 0.376 5.457 0.489 5.797 0.874 5.936 1.049 6.161 1.327 6.697 2.054 6.731 2.077 6.775 2.138 8.442 4.744 9.769 7.068 9.861 7.104

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 and Y data labels to be printed on the report graphs.

Label for first independent data (X)

Label for dependent data (Y)

Graph scaling and lin/log control

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

X Scale (2D Only)		Y 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

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

Scientific Notation

Here you can control scientific notation on the data plots.

X Control		Y Control
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.


y = a1 / (1.0 + exp(-(x-b1)/c1)) + a2 / (1.0 + exp(-(x-b2)/c2)) + a3 / (1.0 + exp(-(x-b3)/c3))
y = y / (k * exp(x))

a1
b1
c1
a2
b2
c2
a3
b3
c3
k

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.

y = a1 / (1.0 + exp(-(x-b1)/c1)) + a2 / (1.0 + exp(-(x-b2)/c2)) + a3 / (1.0 + exp(-(x-b3)/c3))
y = y / (k * exp(x))

a1
b1
c1
a2
b2
c2
a3
b3
c3
k

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.


y = a1 / (1.0 + exp(-(x-b1)/c1)) + a2 / (1.0 + exp(-(x-b2)/c2)) + a3 / (1.0 + exp(-(x-b3)/c3))
y = y / (k * exp(x))

	Upper     Bound	Lower     Bound
a1
b1
c1
a2
b2
c2
a3
b3
c3
k

3D Data Point Size Options

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

3D Point Size