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<br />" <br /> <br />; 'j':J " <br />v U .... \L:.., :) <br /> <br />~ <br />'ii) <br />c <br />CI) <br />- <br />.: <br />~ <br />:c <br />III <br />.c <br />l:! <br />Q. <br /> <br />0.8 <br />0.72 <br />0.64 <br />0.56 <br />0.48 <br />p( y) 0.4 <br /> <br />0.32 <br />0.24 <br />0.16 <br />0.08 <br /> <br />o 6 <br /> <br />LP III Probability Distribution <br /> <br /> ~ r-.... <br /> / '\ <br /> II \ <br /> / 1\ <br /> / \ <br /> I \ <br /> / \. <br /> / " <br />I " <br />~ ...... -- <br /> <br />6.4 6.8 7.2 7.6 8 8.4 8.8 9.2 9.6 10 <br />Y <br />In (flow) <br /> <br />The area under any probibility distribution curve must equal 1.0. This can be used as a check on the <br />procedure <br /> <br />f50 <br />~ ~-1 <br />A. ,(y - e) ,exp[-A.'(Y - e)] <br />1(~) dy=1 <br />E <br /> <br />Check : Area under curve equals 1.0 <br /> <br />Cumulative LP III distribution <br /> <br />In order to get a cumulative LP Type III distribution we can integrate the probability distribution from the <br />lower bound to some value x. The value obtained for each x is the probability that future flow values <br />will be less than or equal to that value of x. <br /> <br />yy := 1,1.1.. 10 <br /> <br />fyy <br />~ ~-1 <br />(A.) '(y - E) ,exp[-A.'(Y - e)] <br />cum_LP _III (yy) := dy <br />1(~) <br />E <br /> <br />cumulative LP III Distribution <br /> <br />In order to see how well the data actually fit the analytical distribution we will sort the logs of the data, <br />compute the respective probabilities and plot the data itself on the same set of axis as the cumulative <br />distribution. <br /> <br />log pearson type 11\ example problem <br />bulletin 17.mcd <br />last save 11/1412003/12:01 PM <br /> <br />30f5 <br /> <br />C:\MyFiles\Mathcad application areas <br />\Statistics\log Pearson Type III aka Gamma <br />distribution files\ <br />11/14/2003/12:07 PM <br />