Part 1: How to Get Maximum Value From Your Curve-Fitting Data
This first article in our Best Practice series examines ways to increase confidence in the accuracy of curve-fitting methods and describes how to avoid some of the common causes of unsuccessful curve fits.
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Part 2: Resolving Fitting Issues
The second article in our Best Practice series explores different ways to solve curve fitting issues, such as using ‘knock out’ of outlying data points to ensure an accurate fit. This article also examines the criteria used to select the correct fit model to ensure the best results.
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Part 3: Fitting Data
The third article in the Best Practice series explores the relative advantages and disadvantages of various fitting methodologies and provides guidance to users on selection of the most suitable fit models.
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Part 4: Parameters and Residuals
The fourth article in the Best Practice series explains the importance of parameters and their starting values in the fitting process. The concept of residuals, the criteria used to determine ‘goodness of fit’, is also introduced.
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Part 5: Robust Fitting and Complex Models
Article 5 describes why robust fitting is becoming a popular method of fitting data while neutralizing the effect of outliers in a data set. Some complex models are also introduced, outlining how a researcher can extend data fitting and analysis beyond basic Michaelis-Menten and dose response models.
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Part 6: Global and Three-Dimensional Fitting
The sixth and last article in the Best Practice series describes how global fitting combines multiple fits to find the optimum global parameter value for each of the fits. Areas of application for three-dimensional fitting are also explored and compared with previous two-dimensional techniques.
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