By Enni S.

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**Additional info for A 1-(S,T)-edge-connectivity augmentation algorithm**

**Example text**

In terms of making the histogram of the number of measurements in each bin, the total number of example counts is 100 — the number of trials (bins). I make the histogram shown in Fig. 3 by counting the number bins with 1 count, 2 counts, 3 counts, etc. 12574. 574. In the trial I ran, I got 10 counts 9 times. The complete histogram for my example is shown below. The bars are the data and the smooth curve is the theoretical result calculated using the binomial formula. 50 Processing Random Data: Statistics for Scientists and Engineers I I CD E 10 12 14 16 18 20 Counts Fig.

4. The parameters n, m, and fi are used in exactly the same way. 18 Processing Random Data: Statistics for Scientists and Engineers Suppose you select 11 numbers from a continuous uniform random number population on the interval [0,1]. 25] is 1/4. 25]? 26. The binomial distribution is most often encountered when you have a known number of independent trials of a phenomenon whose output can be one of two choices. 4. The binomial distribution is also appropriate when you have an experiment where n outcomes are distributed among A; possibilities.

The Poisson distribution is used when you know the average number of outcomes and you want to know what the probability of a particular outcome is. e. p(m)= ( ^ ) ^ ( l - ^ ) — « ^ e X p [ - H - I often use this approximation when doing calculations on histograms. For example, calculate the probability of there being 10 counts in an interval for a uniform distribution divided into 100 bins and 1000 trials. 01. 1257. 1251. 10! Close enough. 22 Processing Random Data: Statistics for Scientists and Engineers Computation of the moments of this distribution is rather straightforward.