x = read(dfile) n = nrows(x) fct ll(y,z) = sum(i,1,n,log(lnormd(x[i],y,z))) y = 0; z = 1; constraints q = {y > -5, y < 10, z > .5} maximize(ll,y,z,q) The function value is: -3.385912e+02 Argument(s): (5.011714e+00 1.978510e+00 ) Gradient: (-6.153095e-08 1.545697e-07 ) ecode = 0, Converged. # of function evaluations: 33 # of gradient evaluations: 40 # of Quasi-Newton iterations: 18 There are no active constraints. = -338.591243 fct lf(x) = lnormf(x,y,z) draw stepgraph(cdf(x)) draw points(lf,0:maxv(x)!160) view

ks1t(x,lf) /* test if x is data from lf */ [K-S-test: are the samples in M plausibly drawn from the distribution F?] null hypothesis H0: M is drawn from the distribution F. The scaled maximum deviation between cdf(M) and F is distributed as the Kolmogorov-Smirnov K statistic. The K-statistic value = 0.494806 at the point 135.087720 The probability P(K > 0.494806) = 0.967170 This means that a value of K larger than 0.494806 arises about 96.717001 percent of the time, given H0. : a 5 by 1 matrix 1: .494806371 2: .494806371 3: .489304956 4: .967170007 5: 135.08772

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