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text version ofAmos, an example of which is included in Graham (in press). It is also straightforward in LISREL and other text-based FIML procedures. Latent-Variable Regression With Amos or With LISREL/EQS and Multiple Imputation A latent-variable example is beyond the scope of this chapter. However, we would like to make a few points in this regard. First, the extension to latent-variable analysis is trivial in Amos (assuming prior knowledge of SEM). One simply performs the analysis with individual variables as indicators of latent variables rather than with the composite indices. Amos handles the rest. The model for including additional variables is the same as previously described. With LISREL 8.50 (Mx, and the FIML aspect of Mplus), latent-variable models with incomplete data are a trivial extension of latent variable models with complete data. With LISREL, one simply adds the statement MI = 9 (assuming the missing value indicator is 9) to the Data Parameters statement. As previously mentioned, the addition of missingdata relevant variables to the model is straightforward. For using multiple imputation with EQS 5.x (Bentler, 1986) and Mplus (Muth n, 2001), the situation is a little more complicated, but no more so than the analysis with SPSS previously described in this chapter. Utility programs have been written to use these two programs with NORM (Schafer, 1997). The two utilities, NORMEQS and NORMplus, make use of a single EQS 5.x or Mplus 2 input le to read and analyze the multiple imputed data sets, combine the results, and provide MI inference. These utilities, along with user guides, can be obtained free at our web site.

Example 1.8: Determine the a priori and a posteriori entropies for the channel of Example 1.7. The a priori entropy is equal to H (X ) = 4 log2 5 5 4 + 1 log2 (5) = 0.7219 bits 5

w=a 1=2 exp4 36:6

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A finance dashboard contains all relevant metrics for the Chief Financial Officer (CFO) and the Finance and Accounting areas. Typical finance metrics could include current sales by various geographic regions and business units, cost of sales, accounts receivable and payable, cash flow, profit and loss (P&L), cost center, budget versus actual, as well as regulatory compliance metrics. Depending on the level and area of an individual s responsibility, the metrics would be presented for that area at appropriate aggregate levels with security to block nonprivileged metrics. The following are some of the financial areas that may be included within a finance dashboard: Financial statements. Income sheet, balance sheet, and cash flow Audit control. Journal analysis and debt analysis Cost control. Departmental cost, budget versus expenses Activity-based costing. Cost centers and cost activities Accounts receivable. Collections, risks, and discount analysis Accounts payable. Cash and discount optimization, supplier data sheet Expense cycle. Aging, cash out flow, and purchase requisition Revenue cycle. Revenue ladder, funnel, and product backlog Regulatory compliance and audit controls

The MT::Object class provides all of the basic methods necessary to store and retrieve the data from the object itself. A separate class named MT::ObjectDriver (and child classes for each supported database, such as MySQL, Postgres, and BerkeleyDB) is used to marshal these objects to and from the underlying database.

Journal of Pediatric Psychology, 21, 499 504 Carey, M (2001, April) Neuropsychological functioning and social functioning in children treated for brain tumors: Evidence of nonverbal learning disabilities Dissertation Abstracts International, 61(9-B), 4974 Chadwick, O, Rutter, M, Shaffer, D, & Shrout, P E (1981) A prospective study of children with head injuries IV: Speci c cognitive de cits Journal of Clinical Neuropsychology, 3, 101 10 1 Chambless, D L, & Hollon, S D (1998) De ning empirically supported therapies Journal of Consulting and Clinical Psychology, 66, 7 18 Chen, E, Craske, M G, Katz, E, Schwartz, E, & Zeltzer, L (2000) Pain sensitive temperament: Does it predict procedural distress and response to psychological treatment among children with cancer Journal of Pediatric Psychology, 25, 269 278 Chen, E, Joseph, M, & Zeltzer, L (2000).

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