It then calculates a Chi Square value that represents the sum of the squared error between the original data and the calculated fit. KaleidaGraph starts with the initial guesses for the unknown parameters that were supplied with the equation. The General curve fit is based on the Levenberg-Marquardt algorithm and is calculated using an iterative procedure. This is the most powerful fitting option in KaleidaGraph because you can specify virtually any equation to be fitted to the data. Using this function, you can define your own equation or choose one from our library of over 100 curve fit definitions. 1.1 Purpose of Curve Fitting 5Ħ The KaleidaGraph Guide to Curve Fitting Nonlinear Curve Fits Nonlinear curve fitting is accommodated in KaleidaGraph through the General curve fit function. Section 2.1 contains a description of each fit and Section 2.2 provides an example of applying a Polynomial curve fit. The five Least Squares fits available in KaleidaGraph are: Linear, Polynomial, Exponential, Logarithmic, and Power. KaleidaGraph s Data Selection tool provides a simple method of graphically removing outliers from a plot. For this reason, the data should always be examined for reasonableness before fitting. If a data point is widely different from the majority of the data, it can skew the results of the regression. The major weakness of the Least Squared method is its sensitivity to outliers in the data. While this technique may not be the most statistically robust method of fitting a function to a data set, it has the advantage of being relatively simple (in terms of required computing power) and of being well understood. Least Squares minimizes the square of the error between the original data and the values predicted by the equation.
This section provides an overview of each category Least Squares Curve Fits Least Squares is a method of curve fitting that has been popular for a long time. 1.2 Types of Curve Fits The curve fits included in KaleidaGraph can be divided into three main categories: Least Squares curve fits, nonlinear curve fits, and smoothing curve fits. KaleidaGraph provides curve fits that can be used in both of these scenarios. Instead, you may just want to use a curve fit to smooth the data and improve the appearance of your plot. In some cases, you may not be concerned about finding an equation. Most of the time, the curve fit will produce an equation that can be used to find points anywhere along the curve.
KALEIDAGRAPH 4.1 SERIES
1.1 Purpose of Curve Fitting Curve fitting, also known as regression analysis, is used to find the "best fit" line or curve for a series of data points. The types of curve fits that are available in KaleidaGraph. It includes information on the following topics: The purpose of performing curve fits. 9 Power Applying a Least Squares Fit Chapter 3 Using Nonlinear Curve Fits 3.1 Introduction to the General Curve Fit General Curve Fit Basics Curve Fit Definition Initial Conditions Curve Fit Definition Dialog Managing the Curve Fit List Using the General Curve Fit Using the Predefined Curve Fit Definitions Entering a Custom Curve Fit Definition Applying a General Curve Fit Weighting Data Fitting Equations with Multiple Independent Variables Setting Limits for Curve Fit Parameters The General Curve Fit and the Macro CalculatorĤ The KaleidaGraph Guide to Curve Fitting Chapter 4 Using Smooth Curve Fits 4.1 KaleidaGraph s Smooth Curve Fits Smooth Weighted Cubic Spline Interpolate Applying a Smoothing Fit Chapter 5 Working with Curve Fit Results 5.1 Viewing Curve Fit Results Displaying the Curve Fit Equation Viewing Coefficients Interpreting Curve Fit Results Correlation Coefficients Parameter Errors Chi Square Value Exporting Curve Fit Results Copying the Parameters to the Clipboard or Calculator Copying the Curve Fit Values to the Data Window Copying the Residuals to the Data Window Using Formula Entry to Obtain Values from a Curve Fit Getting Curve Fit Values into the Data Window Using the Calculated Equation to Find Values Chapter 6 Other Curve Fit Features Appendixes 6.1 Changing Appearance of Curve Fit Lines Displaying Only the Curve Fit Extrapolating to the Axis Limits Forcing a Linear Fit Through the Origin Increasing the Number of Curve Fit Points Removing a Curve Fit Appendix A Troubleshooting Curve Fit Problems Appendix B Reference Information Indexĥ 1Curve Fitting Overview Chapter 1 This chapter provides an introduction to curve fitting.
6 Chapter 2 Using Least Squares Curve Fits 2.1 KaleidaGraph s Least Squares Curve Fits. 6 Smoothing Curve Fits Choosing a Curve Fit Model. 1 The KaleidaGraph Guide to Curve Fittingģ Contents Chapter 1 Curve Fitting Overview 1.1 Purpose of Curve Fitting Types of Curve Fits.
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