Common Problems In Curve Fitting

Single Outlier At End Point High resolution animation and still images	Outliers are often caused by manual errors in recording experimental data.
Single Outlier At Mid Point High resolution animation and still images	Outliers are often caused by manual errors in recording experimental data.
Fitting Parallel Data High resolution animation and still images	This problem is often caused by environmental changes during data collection, such as temperature changes on different days when making multiple data collection runs.
Data With A Large Step High resolution animation and still images	This problem is often caused by environmental changes during data collection, such as a temperature change during a lunch break.
Data With A Poorly Defined Region High resolution animation and still images	This problem can be mitigated by taking additional data in the region that is poorly defined.
Equation Missing An Offset High resolution animation and still images	This illustrates the effect of fitting data with an offset to an equation that does not have one. This problem can be caused by experimental equipment introducing bias (such as a DC offset) during data acquisition. Fitting the data to an equation with an offset will reveal the bias.
Data Scatter Over Entire Range High resolution animation and still images	The effect of data scatter (noise) can be reduced by increasing the total number of data points.
Fitting Random Data High resolution animation and still images	This illustrates the effect of fitting completely random data that has no relationship of any kind.

Based on the pyeq3 CommonProblems example at BitBucket written in Python 3.

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