Scatterplot smoothing
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In statistics various smoothing methods are available to fit a function through the points of a scatterplot to best represent the relationship between the variables.
Scatterplots may be smoothed by fitting a line to the data points in a diagram. This line attempts to display the non-random component of the association between the variables in a 2D scatter plot.
Smoothing is normally accomplished by using any one of the techniques mentioned below.
- A straight line
- A quadratic or a polynomial curve
- Smoothing splines
Of these the smoothing splines technique is applied when greater flexibility is needed in nonlinear associations. The curve is fitted in such a way that provides the best fit, often defined as the fit that results in the minimum sum of the squared errors (least squares criterion).
The use of smoothing seperates the non-random data from the random fluctuations and allows to predict the response based value of the explanatory variable.[1]