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 €œtrue€ 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 044501 .958 .415 872 329 786 .243 .700 57 614 .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 .717 174 631 .088 .545 .002 459