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<math>\sigma^2_{\epsilon i\_w} = \frac{n_{i}}{n_{i}-1} \cdot \left ( \frac{\sum^{n_{i}}_{j}w_{j,i} x^2_{j,i}}{\sum^{n_{i}}_{j}w_{j,i}} - \frac{\left ( \sum^{n_{i}}_{j}w_{j,i}x_{j,i} \right )^2}{\sum^{n_{i}}_{j}w_{j,i}} \right ) / \frac{n_{i}}{2} </math> | <math>\sigma^2_{\epsilon i\_w} = \frac{n_{i}}{n_{i}-1} \cdot \left ( \frac{\sum^{n_{i}}_{j}w_{j,i} x^2_{j,i}}{\sum^{n_{i}}_{j}w_{j,i}} - \frac{\left ( \sum^{n_{i}}_{j}w_{j,i}x_{j,i} \right )^2}{\sum^{n_{i}}_{j}w_{j,i}} \right ) / \frac{n_{i}}{2} </math> | ||
These " weighted variances of means" are averaged over all unique reflections of the resolution shell: | |||
<math>\sigma^2_{\epsilon_w}=\sum^N_{i} \sigma^2_{\epsilon i\_w} / N </math> | |||
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