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As described in Part Three, ABM yields a rich amount of pro t margin layer data that provides insights as to where, on which products, and with whom the company is making or losing money. But this data is historical and hence limited to being descriptive. It does, however, communicate where previously committed money was spent, and it reports past spending in the form of outputs and outcomes. When output costs are considered as feedback, this data becomes very useful for taking corrective actions and making adjustments. Decision making is about predictive testing for whether future spending opportunities (spending that will include investors and lenders precious capital) should be committed. Similar to the way in which ABM data is combined for what-if scenarios, decisions to enhance value going forward use evaluative measures as the springboard and to that add predictive measures. 23 This involves classical investment justi cation and capital budgeting analysis. It applies return on investment (ROI) analysis with discounted cash ows (DCF methods) all techniques that readers with MBAs learned in graduate school. This forwardlooking logic aims to test whether all the money being spent now and going forward (which has gone into or will go into speci c projects, products, service lines, and customers) will generate economic pro ts, not just accounting pro ts.

However, if the card permits the rst use after each payroll to be free, this may satisfy some states Payroll must research this carefully before signing with any vendors Does the state have pay stub requirements that must be met Many states have requirements that a pay stub or statement be issued with each paycheck Paycard vendors may offer an electronic monthly statement or pay stub In tough states like California, this would not be in compliance with state regulations Before trying to go completely paperless when implementing a paycard program, the payroll department needs to verify if.

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Figure 7.8 illustrates the TCG documentation roadmap, which includes an architectural overview, the main speci cation (which de nes the trusted platform module), the trusted software stack speci cation, the platform-speci c design guide, and a collection of platformspeci c speci cations (for example, PC and PDA). Trusted Platform Module (TPM) The Trusted Platform Module (TPM) speci cation is included in the TCG main speci cation. It is a hardware component that provides the following functionality (Backgrounder 2003): Asymmetric key functions for on-chip key pair generation using a hardware random number generator; private key signatures; and public key encryption and private key decryption of keys enable more secure storage of les and digital secrets. Secure storage of hash values representing platform con guration information in Platform Control Registers (PCRs) and secure reporting of these values, as authorized by the platform owner, in order to enable veri able attestation of the platform con guration based on the chain of trust used in creating the HASH values. This includes creation of Attestation Identity Keys (AIKs) that cannot be used unless a PCR value is the same as it was when the AIK was created.

to capture the business and how much it could take back through short-term maneuvering. The phenomenon of last-minute price increases was not a new one. Chinese manufacturers have actually been at it for quite some time. One of the rst journalists to enter China following Chairman Mao s revolution was an Australian newspaperwoman named Lisa Hobbs. She arrived in 1965, and among other places spent a good deal of time at the Canton Fair. In her book, I Saw Red China, she described what the Chinese government did to importers in those days:

Vars: volMeasure(1),count(0),dayName("Monday"); Arrays: binArray[5](0),dayCntArray[5](0); {binArray will keep track of the daily ATR ratios binArray[0] = Monday binArray[1] = Tuesday binArray[2] = Wednesday binArray[3] = Thursday binArray[4] = Friday Remember, arrays are zero based in EasyLanguage} if(DayOfWeek(Date) = Monday) then volMeasure = AvgTrueRange(30)[1]; {The DayOfWeek function returns 1-5 for Monday-Friday. We subtract a 1 from whatever the function returns to make the arrays zero based. If today is Monday, then DayOfWeek returns a 1. We then subtract 1 from that and use it as the index into our arrays. Mondays will be stored in the zero element.} binArray[DayOfWeek(Date)-1] = binArray[DayOfWeek(Date)-1] + AvgTrueRange(1)/volMeasure; dayCntArray[DayOfWeek(Date)-1] = dayCntArray[DayOfWeek(Date)-1] + 1; if(lastBarOnChart) then begin Print(SymbolName,Date:6:0,"Day of Week Volatility Study"); for count = 0 to 4 begin if(count = 0) then dayName = "Monday"; if(count = 1) then dayName = "Tuesday"; if(count = 2) then dayName = "Wednesday"; if(count = 3) then dayName = "Thursday"; if(count = 4) then dayName = "Friday"; Print(dayName,binArray[count]/dayCntArray[count]); end; end;

Clearly, the solution is qML = S T yi/T, which is the sample mean. i=1 Next, consider Score(v), where 2v cancels from the denominator, leaving -T + S T [yi - q]2/v = 0. Substituting qML solution of the rst score equation, we i=1 have the ML estimate of v given by vML = (1/T) S T [yi - qML]2. Thus the ML i=1 estimators of q and v = s2 are

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