CC1/2: Difference between revisions

41 bytes added ,  7 April 2019
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<math>\sigma^2_{\epsilon_w}=\sum^N_{i} \sigma^2_{\epsilon i\_w} / N </math>  
<math>\sigma^2_{\epsilon_w}=\sum^N_{i} \sigma^2_{\epsilon i\_w} / N </math>  


It should be noted that it is not straightforward to define the correct way to calculate a weighted variance (and the weighted variance of the mean). The formula <math>s^2_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}} -\left ( \frac{ \sum^{n_{i}}_{j}w_{j,i}x_{j,i} }{\sum^{n_{i}}_{j}w_{j,i}}\right )^2 \right )</math> is also found at [https://www.itl.nist.gov/div898/software/dataplot/refman2/ch2/weightsd.pdf]
It should be noted that it is not straightforward to define the correct way to calculate a weighted variance (and the weighted variance of the mean). The formula <math>s^2_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}} -\left ( \frac{ \sum^{n_{i}}_{j}w_{j,i}x_{j,i} }{\sum^{n_{i}}_{j}w_{j,i}}\right )^2 \right )</math> is - after some manipulation - the same as that found at [https://www.itl.nist.gov/div898/software/dataplot/refman2/ch2/weightsd.pdf].


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