One of the most widely used goodness-of-fit tests is the Kolmogorov-Smirnov (K-S) family of tests which have been implemented by many computer statistical software packages. To calculate a p value (evaluate the cumulative sampling distribution), these packages use various methods including recursion formulae, limiting distributions, and approximations of unknown accuracy developed over thirty years ago. Based on an extensive literature search for the one-sided one-sample K-S test, this paper identifies two direct formulae and five recursion formulae that can be used to calculate a p value and then develops two additional direct formulae and four iterative versions of the direct formulae for a total of thirteen formulae. To ensure accurate calculation by avoiding catastrophic cancelation and eliminating rounding error, each formula is implemented in rational arithmetic. Linear search is used to calculate the inverse of the cumulative sampling distribution (find the confidence interval bandwidth). Extensive tables of bandwidths are presented for sample sizes up to 2, 000. The results confirm the hypothesis that as the number of digits in the numerator and denominator integers of the rational number test statistic increases, the computation time also increases. In comparing the computational times of the thirteen formulae, the direct formulae are slightly faster than their iterative versions and much faster than all the recursion formulae. Computational times for the fastest formula are given for sample sizes up to fifty thousand.

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This study presents findings from a survey administered in November 1975 among a sample of households in southeastern Wisconsin to determine, among other things, the shifts in travel vacations that occurred in response to the higher fuel prices that developed between 1973 and 1975 and that might occur in response to the alternative futures of still higher fuel prices and restricted fuel availability. Specific socioeconomic variables significantly distinguished between households that changed vacation plans and those that did not make changes between 1973 and 1975. We found that the following variables significantly influenced vacation travel: occupation of the household head, household income, education level of the household head, age of the household head, number of children in the household aged 15 and under, and geographic location of the household. Under futuristic alternatives of either higher prices or restructed fuel availability households were willing to experiment with various vacation adjustment strategies. This indicates a lack of clear preference among households for any one strategy.

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