# CSharp - CurveFitting

Settings
 Data Points: 5.8 22.38 6.65 27.56 7.5 33.12 8.34 39.01 9.19 45.23 10.04 51.76 10.89 58.59
Order:
 X1 Y1X2 Y2X3 Y3∙∙ ∙∙ X1 X2 X3 ∙∙Y1 Y2 Y3 ∙∙ Y1 X1Y2 X2Y3 X3∙∙ ∙∙ Y1 Y2 Y3 ∙∙X1 X2 X3 ∙∙
Decimal:
Begin: End: Divs:
X-axis:
Y-axis:
Width: Height:
Image:

Curve Fit
 Method: Linear regression of straight line, y = a * x + b Linear regression of exponential equation, y = a * exp(b * x) Linear regression of power equation, y = a * pow(x, b) Linear regression of saturation-growth-rate equation, y = a * x / (x + b) 2-degree polynomial regression, y = a0 + a1 * x + a2 * x^2 3-degree polynomial regression, y = a0 + a1 * x + a2 * x^2 + a3 * x^3 4-degree polynomial regression, y = a0 + a1 * x + ... + a4 * x^4 5-degree polynomial regression, y = a0 + a1 * x + ... + a5 * x^5 6-degree polynomial regression, y = a0 + a1 * x + ... + a6 * x^6 7-degree polynomial regression, y = a0 + a1 * x + ... + a7 * x^7 8-degree polynomial regression, y = a0 + a1 * x + ... + a8 * x^8 9-degree polynomial regression, y = a0 + a1 * x + ... + a9 * x^9 10-degree polynomial regression, y = a0 + a1 * x + ... + a10 * x^10 11-degree polynomial regression, y = a0 + a1 * x + ... + a11 * x^11 12-degree polynomial regression, y = a0 + a1 * x + ... + a12 * x^12 Digits: 3 digits 4 digits 5 digits 6 digits 7 digits 8 digits 9 digits 10 digits 11 digits 12 digits 13 digits 14 digits Remove near 0
Function
 y =
Graph
 ▲ + − ▼ ! ◄ + − ►

 y = 7.124823630278 * x - 19.787278320648

 x' data y' data y = f(x') y' - y 5.8 22.38 21.536698734964 0.8433012650356 6.65 27.56 27.592798820701 -0.032798820700702 7.5 33.12 33.648898906437 -0.528898906437 8.34 39.01 39.633750755871 -0.62375075587052 9.19 45.23 45.689850841607 -0.45985084160682 10.04 51.76 51.745950927343 0.014049072656881 10.89 58.59 57.802051013079 0.78794898692058

 Standard error of the estimate Sy/x 0.665365 Coefficient of determination r2 0.997838 Correlation coefficient r 0.998919

### Curve fitting with image draw of function graph

This C# ASP.NET page uses different curve fitting algorithms to find a mathematical function, y = f(x), for a set of x and y data points.

Note that some methods do not accept zero or negative numbers as x and/or y values.

The function y = f(x) may also be changed manually and compared with the data points.

See the page FunctionGraph for help and notes about mathematical expressions, syntaxes and graph tool buttons.

Clicking any OK button will save the inputs in the current session, until the web browser is closed.

No third-party add-ons or softwares are used on this page, only plain HTML with an image is used.

Copyright © 1996-2022 Scandinavian Digital Systems AB
Developed by Anders Danielsson