Computer-Intensive Methods for Testing Hypotheses: An Introduction
How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.
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Approximate Randomization Tests
Monte Carlo Sampling
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100 CONTINUE 1844 presidential election Appendix approximate randomization test assess the significance auxiliary randomization betacf betai page 122 bootstrap methods bootstrap sampling distribution bootstrap std bootstrap test CALL Confidence(nge%,NS compute computer-intensive methods confidence interval Confidence page 156 confidence(nge,NS conventional parametric tests correlation dependent variable Desired number example expected value following subroutines FORTRAN gammaln GOSUB ComputeStatistic hypothesis is rejected hypothesis is true hypothesis test idum Insert the following integer joint bootstrap longint Monte Carlo method Monte Carlo sampling nge+1 nge+l)/(NS+l nontransfer students Normal approximation method null hypothesis population number of samples number of shuffles Numerical Recipes Software observations permutations PRINT PseudoStat>=ActualStat QuickDraw random number random sample random variable RandomNumberGenerator READLN rejecting the null rejection level relative slave holdings Results Actual value sample mean seconds Mac seconds Partial listing shift method bootstrap significance level Significance test execution simulated standard Normal strata stratum Table temp test statistic transfer students valid vector WRITELN