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Caruso and Cliff (1997) studied a number of inferential methods based on rs and found the Equation 2526 z test performed the best in terms of actual Type I error rate and power Although Equation 2526 assumes bivariate normality, it does have at least one advantage over its counterpart based on Pearson s r (which requires the same assumption) Equation 2526 will yield the same result for any data that is or can be monotonically transformed to bivariate normality because rs is invariant under such a transformation In this regard, Equation 2526 is more general than is the corresponding test based on Pearson s r Recently, Bonett and Wright (2000) presented interval estimates based on rs (and ta ) under rather general assumptions They base their interval estimate for s on the variance term [(1 + rs )/2]/(n 3) and offer evidence for its accuracy.

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Each node records the duration for which the object is in its range. Neighboring nodes exchange these times and their locations. For each point in time, the object s estimated position is computed as a weighted average of the detecting nodes locations. A line- tting algorithm is run on the resulting set of points.

Hotel Theoretical Income Statement For the Year Ending December 31, 0006 Net Revenue Departmental Income (Loss) Rooms Food Beverage Miscellaneous income Operating Department Totals Undistributed Operating Expenses Administrative and general Marketing Property operation and maintenance Energy costs Total Undistributed Operating Expenses Income before Fixed Charges Fixed Charges Property taxes Insurance Interest Depreciation Operating Income (before tax) Income tax Net Income

Non-Null Inference With Tau-a The variance term for tau-a presented previously assumes that X and Y are completely independent Complete independence is usually of limited interest to the applied researcher because it is not possible to test a general null hypothesis such as H0: a = c, nor is it possible to compute a con dence interval for a These failings are in con ict with general recommendations for statistical analysis in psychology and education (Cohen, 1994; Wilkinson & the Task Force on Statistical Inference, 1999) It is possible to proceed as we do with parametric methods and estimate the characteristics of the sampling distribution of ta from sample data Speci cally, when a = 0, we can compute ta , estimate its variance, and use these quantities as a basis for a general hypothesis test or a con dence interval.

Under H0: a = 0, the normal distribution variate is the sample tau-a over the square root of Equation 25.24, z= ta

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Specific Item Cost The specific identification method records the actual cost of each item. In Exhibit 2.4(a), 10 items remain in stock at month end 2 from the purchase of June 2, 4 from the purchase of June 15, and 4 from the purchase of June 28. The value of ending inventory (EI) on June 30 would be (2 @ $20) $40 (4 @ $22) $88 (4 @ $19) EI $76 $204 Total EI

A number of estimates of the variance of ta have been developed for use when a = 0 (Cliff & Charlin, 1991; Daniels & Kendall, 1947; Kendall, 1970) One such variance estimate is the consistent estimate It is simpler to discuss the consistent estimate if we rst de ne the dominance product tihxy = (dihx )(dihy ), (2528).

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