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In the example given, the assumption was made that net annual saving is the same for each of the five years. In reality, this might not always be the case. For example, there might be expenses in year 0001 (or in any of the other years) that are nonrecurring for example, training costs or a major overhaul. Alternatively, the amount of an expense might change over the period for example, depreciation computed using double-declining balance. To take care of this, we project total savings and total costs for each year for the entire period under review. We add up the annual net savings to give us the net saving figure for the entire period. This net saving figure for the entire period can then be divided by the number of years of the project to give an average annual net savings figure to be used in the equation. Let us illustrate this for Machine A only. Savings and expenses are as in Exhibit 12.1, except that in year 0003 there will be a special overhaul cost of $1,000, and the double-declining balance method of depreciation (rather than straight line) will be used. Since the asset has a five-year life, the depreciation rate is 40 percent. Exhibit 12.2 shows the results. Total net saving over the five-year period will be the sum of the individual years savings. This amounts to $7,000. The average annual net saving will be $7,000 divided by 5 equals $1,400. ARR $1,400 $3,000 46.7%

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Several aspects of the business component concept are mainly relevant in the context of collaboration within a system-for example, the functional categories . The system-level conceptual framework and terminology addressed in this chapter allow the discussion of these aspects to be completed . This chapter presents many examples that illustrate the concepts discussed . The architectural and business modeling principles behind the examples given are not addressed, however, until 9, "Application Architecture," and 11, "Component-Based Business Modeling," respectively . Hence this chapter makes a number of significant assumptions about the structure of the system, its architecture, and what a "well-formed" system looks like . This means that, for some readers, some of the examples may appear to be arguable or may give rise to questions about structure and modeling that are not immediately answered . We ask those readers to bear with us while we build the conceptual framework.

where the convergence takes place with probability 1 for all 2k subsets, S {X (1) , X (2) , . . . , X (k) }.

Essential Windows CE Application Programming by Robert Burdick Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471327476 Pub Date: 03/01/99

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Acceptance of Risky Behavior model (Figure 242) as an example The section concludes with discussion and illustration of the computer process of model speci cation using the EQS SEM software program Model Hypotheses and Diagrams In this phase of the process, the model is speci ed; that is, the speci c set of hypotheses to be tested is given This is done most frequently through a diagram The Acceptance of Risky Behavior diagram given in Figure 242 is an example of hypothesis speci cation.

This example has ve measured variables: (a) FAMILY_S, the average of three responses to statements about bonds to family, (b) TEACH_SC, the average of three responses to statements that query bonds to teachers, (c) OKDRINK2, a Likert scale measure (1 5) of degree of endorsement of the statement It is OK to drink, (d) OKSMOKE2, a Likert scale (1 5) measure of degree of agreement with the statement It is OK to smoke, and (e) gender (boys = 1, girls = 2) The example of Figure 242 contains some asterisks and a 1 The asterisks indicate parameters (variances and covariances of IVs and regression coef cients) to be estimated The variances of IVs are parameters of the model and are estimated or xed to a particular value.

if det (X) = 0.

The number 1 indicates that a parameter, either a path coef cient or a variance, has been set ( xed) to the value of 1 (The rationale behind xing paths is discussed later in the section about identi cation) Our example contains two hypothesized latent variables (factors): Weak Institutional Bonds (WK_BONDS), and Acceptance of Risky Behavior (ACCEPT_RISK) The Weak Institutional Bonds (WK_BONDS) factor is hypothesized to have two indicators, bonds to family (FAMILY_S) and bonds to teachers (TEACH_SC) Weaker Institutional Bonds predicts weaker bonds to both family and teachers Note that the direction of the prediction matches the direction of the arrows The Acceptance of Risky Behavior (ACCEPT_RISK) factor also has two indicators; degree of endorsement to two statements, It is OK to drink (OKDRINK2) and It is OK to smoke (OKSMOKE2) Greater Acceptance of Risky Behavior predicts higher scores on both the alcohol- and tobacco-use statements.

Unfortunately, all the above are also used by some digital cameras to indicate resolution levels instead of compression. So, you re going to have to check your camera s manual to learn just where and how to set the JPEG compression on your particular camera.

This model also hypothesizes that both Weak Institutional Bonds and gender are predictive of Acceptance of Risky Behavior Also notice the line with the doubled-headed arrow that directly connects Weak Institutional Bonds and gender This path indicates a hypothesized covariance between these variables Note that this is a covariance and that it does not imply directionality..

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