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Let us explore the implications of these two factors a little further. The water level at any one point in a stream at any speci ed time is called its stage. Unless the head in the adjoining aquifer exceeds the stage, it is impossible for the stream to gain water from groundwater discharge. That groundwater head should exceed stream stage is thus a necessary condition for groundwater discharge to a stream. However, it is not in itself a suf cient condition to guarantee a substantial discharge: the permeability of the channel perimeter is the nal deciding factor. In many geological settings, the sediments which immediately ank the stream channel have a lower hydraulic conductivity than the adjoining aquifer materials (see Box 5.1 for an explanation of why this is often the case). Recalling Darcy s Law (Section 3.2), given that the cross-sectional area of contact between a stream and an adjoining aquifer is xed by the channel dimensions, then, for a given hydraulic gradient between an aquifer and a stream channel, the rate of groundwater discharge to the river is wholly governed by the hydraulic conductivity of the channel perimeter. Of course this works both ways: if the stream bed is of suf ciently low hydraulic conductivity, then stream ow can occur without signi cant loss to the subsurface even where the head in the

was that the individual trial analysis corroborated and extended the previous analyses based on averaged data. Most of the methodological decisions were based on previous research, and this made it possible to compare the results of this experiment with the results of previous research. Some of the methods were not standard, for example, the measures taken on individual trials, the analyses of correlation patterns, and the inferences of cognitive processes based on the correlation patterns. The nonstandard methods led to some advance in knowledge.

From a metabolic point of view, energy use and production in skeletal muscle is simply nothing short of amazing in its range and responsiveness. In an actively exercising animal, muscle can account for as much as 90% of the oxygen consumption in the body. This can represent an increase in the muscle s metabolic rate of as much as 200% from the resting state (Hargreaves and Thompson 1999).

and lognormal The maximum likelihood method with the ARIMA (5, 0, 0) model speci ed produced very accurate results across all conditions It was generally as good or better than the correct model identi cation The maximum likelihood with the ARIMA (1, 0, 0) model generally produced a very good approximation Violations of the distribution assumptions had no effect Computational Issues Analysis of time series data requires the use of a computer program Fortunately, a large number of programs have become available in the last two decades Harrop and Velicer (1990a, 1990b) evaluated ve programs: BMDP (Dixon, 1985), GENTS (Velicer, Fraser, McDonald, & Harrop, 1986), ITSE (Williams & Gottman, 1982), SAS (SAS Institute, 1984), and TSX (Bower & Glass, 1974; Glass, Bower, & Padia, 1974) Simulated data from 44 different ARIMA models were employed to assess the accuracy of the programs (Harrop & Velicer, 1990b).

Three programs produced generally satisfactory results (TSX, GENTS, and SAS) One was inaccurate across a wide range of models (ITSE), and one was occasionally inaccurate and occasionally failed to complete the analysis (BMDP) The original ITSE contained incorrect formulas and an amended version of this program, ITSACORR, is available (Crosbie, 1993) The overall evaluation of the computation features and quality of documentation was not very favorable (Harrop & Velicer, 1990a) Some of the programs evaluated in the Harrop and Velicer (1990a, 1990b) studies have been substantially modi ed since inclusion in the study and time series analysis has since been added to widely used statistical packages, such as SPSS Trends (SPSS, Inc, 1988) and SYSTAT (Wilkinson, 1986) SAS remains one of the best programs, with extensive supporting features However, a new comparative evaluation of time series programs is needed because the Harrop and Velicer studies are dated.

Measurement Issues One of the impediments to the widespread use of time series in the behavioral sciences has been the problem associated with obtaining appropriate quantitative measures on a large number of occasions Measures previously employed for time series analysis include physiological and behavioral measures such as blood pressure, the number of cigarettes smoked per day, and the number of standard drinks per day Many of the measures that have been employed in crosssectional research studies are simply not appropriate for time series designs Assessment batteries were often lengthy and.

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