Combine two Summary's.
Mean squared error. In other words, a biased estimate of variance.
Converts this struct to a MeanSD. Called via alias this when an implicit conversion is attetmpted.
1 Summary summ; 2 summ.put(1); 3 summ.put(2); 4 summ.put(3); 5 summ.put(4); 6 summ.put(5); 7 assert(summ.N == 5); 8 assert(summ.mean == 3); 9 assert(summ.stdev == sqrt(2.5)); 10 assert(summ.var == 2.5); 11 assert(approxEqual(summ.kurtosis, -1.9120)); 12 assert(summ.min == 1); 13 assert(summ.max == 5); 14 assert(summ.sum == 15);
Output range to compute mean, stdev, variance, skewness, kurtosis, min, and max online. Using this struct is relatively expensive, so if you just need mean and/or stdev, try MeanSD or Mean. Getter methods for stdev, var cost a few floating point ops. Getter for mean costs a single branch to check for N == 0. Getters for skewness and kurtosis cost a whole bunch of floating point ops. This struct uses O(1) space and does *NOT* store the individual elements.
Note: This struct can implicitly convert to a MeanSD.
References: Computing Higher-Order Moments Online. http://people.xiph.org/~tterribe/notes/homs.html