# Changeset 586 for trunk/doc

Ignore:
Timestamp:
Jun 19, 2006, 11:56:04 AM (17 years ago)
Message:

closes #23 redesign of regression classes

File:
1 edited

### Legend:

Unmodified
 r494 The first group is when some of the measurements are known to be more precise than others. The more precise a measuremtns is the larger precise than others. The more precise a measurement is, the larger weight it is given. The simplest case is when the weight are given before the measurements and they can be treated as deterministic. It can be treated as independent of the observable. Since there are various origin for a weight occuring in a statistical analysis, there are various way to treat the weights and in general Since there are various origins for a weight occuring in a statistical analysis, there are various ways to treat the weights and in general the analysis should be tailored to treat the weights correctly. We have not chosen one situation for our implementations, so see specific \end{itemize} An important case is when weights are binary (either 1 or 0). Then we get same result using the weighted version as using the data with get the same result using the weighted version as using the data with weight not equal to zero and the non-weighted version. Hence, using binary weights and the weighted version missing values can be treated the variance is estimated as $\sigma_x^2=\frac{\sum w_xw_y(x-m_x)^2}{\sum w_xw_y}$. As in the non-weighted case we define the correlation to be the ratio between the covariance and geomtrical avergae of the variances the correlation to be the ratio between the covariance and geometrical average of the variances \$\frac{\sum w_xw_y(x-m_x)(y-m_y)}{\sqrt{\sum w_xw_y(x-m_x)^2\sum