Kolmogorov-smirnov one-sample test

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Reference no: EM1311744

Kolmogorov-Smirnov one-sample test:

In theory, Monte Carlo studies rely on computers to generate large sets of random numbers. Particularly important are random variables representing the uniform pdf defined over the unit interval, fy(y) = 1,0 ≤ y ≤ 1. In practice, though, computers typically generate pseudorandom numbers, the latter being values produced systematically by sophisticated algorithms that presumably mimic "true" random variables. Below are one hundred pseudorandom numbers from a uniform pdf. Set up and test the appropriate goodness-of-fit hypothesis. Let α = 0.05.

.216	.673	.130	.587	.044	.501	.958	.415	.872	.329
.786	.243	.700	.157	.614	.071,	.528	.985	.442	.899
.356	.813	.270	.727	.184	.641	.098	.555	.012	.469
.926	.383	.840	.297	.754	.211	.668	.125	.582	.039
.496	.953	.410	.867	.324	.781	.238	.695	.152	.609
.066	.523	.980	.437	.894	.351	.808	.265	.722	.179
.636	.093	.550	.007	.464	.921	.378	.835	.292	.749
.206	.663	.120	.577	.034	.491	.948	.405	.862	.319
.776	.233	.690	.147	.604	.061	'.518	.975	.432	.889
.346	.803	.260	.717	.174	.613	.088	.545	.022	.459

Reference no: EM1311744

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