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These points are well illustrated by my optimization study on the two moving average crossover system for the same portfolio of assets in 3 (see Tables 71 to 720) In my optimization study of the two moving average crossover system, I chose to examine shorter-term moving average values between 6 and 10 days using a one-step interval (ie, 6, 7, 8, 9, and 10) and longer-term moving average values set between 20 and 32 days using three-step intervals (ie, 20, 23, 26, 29, and 32) The reason behind choosing a particular set of values in an optimization study is a function of utility, distinctiveness, experience, and common sense For example, for shorter-term moving averages, there is quite a significant difference between a 6- and 7-day moving average (eg, increases the data series by one-sixth).



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Some methods can have either a text result or a numeric result. The reason for this is explained under the analytical method limits, but we must also make sure that we can enter either of these options. The system must not require us to enter both the text values and the numeric values in order to accept the results as fully entered. Instruments have an output measurement with a certain number of digits. These are generally stated according to the accuracy of the measurement. When calculations are involved, the output may have a much higher number of digits. This number bears no resemblance to the accuracy of the result, only to the accuracy of the calculation. From a mathematical viewpoint, 0.5 is the same value as 0.50000. The difference is in the accuracy. A value of 0.5 is between 0.45 and 0.54, while the value of 0.50000 is between 0.49995 and 0.50004. When implementing this in a LIMS, we need to set the number of digits for each measurement and de ne the calculation according to the accuracy of the method. The number of digits must be scienti cally sound and should be based on the method validation. But it is also important that all calculations use all digits, and that only the nal result is rounded to the correct number of digits. If we round after each calculation step and use the rounded value in the next step, the nal result is likely to be incorrect. Table 18.1 tells about digits.





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terms of ef ciency and ease of use It is much easier to see how information is propagated on the hierarchic interface that is, how changing one node can propagate information, node by node, up the levels of the hierarchy A user of the at interface, by constrast, must reconstruct the series of variable that are affected (indeed, the hierarchic structure can be reconstructed from the textual explanations), but it would require much more cognitive effort and time to do so Hence, even though the two interfaces are informationally equivalent, the at interface is sorely lacking in terms of exibility and computational power In general, this is the real power underlying diagrammatic representations: not that it contains more informational power but that it can dramatically increase computational power.

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As with the earlier ASP example, this simple PHP example only scratches the surface of what you can accomplish with PHP. Instead of simply setting a string variable, the code could access a database and present the resulting data in a number of complex forms. PHP is available on Windows and Linux and requires that the PHP processor be installed on the Web server (both operating system and Web server application).

button press. The Spotlet class defines constants for the common buttons that an application would intercept: PAGEUP, PAGEDOWN, CALCICON, MENUICON, KEY_HARD1, KEY_HARD2, KEY_HARD3, KEY_HARD4, and KEY_POWER. Note that the last five button events are trapped only when the input focus option is WANT_SYSTEM_ KEYS.

integrated over the projection R of the region in the xy-plane (Fig. 250).

By contrast, for the longer-term moving average, use of one-step variations in our optimization study is probably not going to yield the same distinctiveness in our data series as will be achieved by staggering our steps by three This is because the difference between a 31- and a 32-day moving average only increases the data series by 1/31st and is therefore minute12 In choosing which values to include and exclude from the study, I began with the exclusion of nonsensical values (eg, 2 days as a value for our short-term moving average) and worked forward based on values used most commonly by technicians in the markets (eg, 7 days for the shorterterm moving average and 29 days for the longer-term moving average).

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