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Each year, top-level management generally prepares long-range budgets for up to five years. They may or may not involve department managers. Each year such budgets are revised for the next period (up to five years) forward. For coordination, the budget committee would be involved. Short-term budgets are prepared annually, for the most part, with monthly projections. Each month, budgets for the remaining months of the year should be revised to adjust for any changed circumstances. Department managers should be involved in such revisions and the budget committee should be involved for overall coordination. The department managers or other supervisory staff usually handle weekly or daily short-range budgets internally. For example, the housekeeping supervisor would schedule housekeepers (which affects the payroll budget) on a daily basis based on anticipated rooms occupancy.

< e (t/e)

such as discriminant analysis also yield approximate weights in any applied analysis because normality can never exactly hold in sample data (nor in the population; eg, see Wilcox, 1991) In the case of predicting from dominances, a truly optimal solution is possible For any set of p predictors, there are a nite number of possible dominance patterns across the predictors (3p if there are ties on the predictors, 2p if not) In principle, these can be used to de ne a set of inequalities on the weights (Cliff, 1994) The least squares weights have been found to satisfy all, or nearly all, of these inequalities in the examples tested However, the number of inequalities rises rapidly with the number of predictors.

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= H (Yn |Yn 1 , . . . , Y1 , X1 , X0 , X 1 , . . . , X k ) (4.59)

A curious consequence of the inequalities is that in the two-predictor case without ties, using two predictors cannot improve on the better of the individual predictors by the criterion of Equation 2542 No Partial Tau In the case of predicting from two dominances, the numerator of the rst OMR weight can be shown to equal t1y t2y t12 This numerator is identical in form to the numerator of the LSMR standardized regression coef cient and the numerator of the Pearson partial correlation Given this similarity, it is tempting to interpret OMR weights in terms of partial relationships as is common in LSMR However, such interpretations are problematic when dealing with dominances The reason is that the investigation of partial relationships is based on the analysis of residuals, and a residualized dominance makes no sense.

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A residual of a dominance score obtained with the regular least squares methods can take on any real value This violates the very nature of dominance as an index of the order relations greater than, less than, and equal to More important is that the residuals of dominances do not behave like residuals of raw interval scores and can cause some curious results for partial tau correlations For example, in situations in which two variables are completely dependent on a third, the partial Pearson correlation will always be zero, but the value of a partial tau correlation can be nonzero (see Nelson & Yang, 1988) The quali cations just discussed indicate the OMR weights should be interpreted only as practical devices for predicting the order on the criterion.

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In OMR, the functional relationship between the dominance on Y and the information on the predictors, dihy = f (aihj ), is much more vague than in LSMR Therefore, the OMR weights cannot be interpreted in any causal or explanatory sense We can safely say that the size of an OMR weight represents the relative importance of a predictor to the overall prediction system A variable with a.

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TABLE 7.6 Component Loadings for the Two Principal Components Extracted from the Training Data Seta

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