Regressing structure function against bin separation

How the regressions are set up depends on the choice of differencing scheme, these are explained below.

Forward-difference scheme regression

1. If [math]\displaystyle{ D_{ll}(n,\delta) }[/math] was evaluated using a forward-difference scheme, the regression is done for the combined data from all bins in the selected range, hence the maximum number of [math]\displaystyle{ D_{ll}(n, \delta) }[/math] values for each separation distance will be the number of bins in the range less 1 for [math]\displaystyle{ \delta }[/math] = 1, reducing by 1 for each increment in [math]\displaystyle{ \delta }[/math], with the regression ultimately yielding a single [math]\displaystyle{ \varepsilon }[/math] value for the data segment

Bin-centered difference scheme regression

1. If [math]\displaystyle{ D_{ll}(n,\delta) }[/math] was evaluated using a bin-centered difference scheme, the regression is usually done for each bin individually, with a single [math]\displaystyle{ D(n, \delta) }[/math] for each separation distance, ultimately yielding an [math]\displaystyle{ \varepsilon }[/math] for each bin.

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