Results Discussions
Results Discussions Sample Clauses
Related to Results Discussions
Discussion Staff has reviewed the proposal relative to all relevant policies and advise that it is reasonably consistent with the intent of the MPS. Attachment B provides an evaluation of the proposed development agreement in relation to the relevant MPS policies.
Justification and Anticipated Results The Privacy Act requires that each matching agreement specify the justification for the program and the anticipated results, including a specific estimate of any savings. 5 U.S.C. § 552a(o)(1)(B).
Financial Condition There shall have been no material adverse change, as determined by Bank, in the financial condition or business of Borrower, nor any material decline, as determined by Bank, in the market value of any collateral required hereunder or a substantial or material portion of the assets of Borrower.
Expected Results VA’s agreement with DoD to provide educational assistance is a statutory requirement of Chapter 1606, Title 10, U.S.C., Chapter 1607, Title 10, U.S.C., Chapter 30, Title 38, U.S.C. and Chapter 33, Title 38, U.S.C (Post-9/11 GI Xxxx). These laws require VA to make payments to eligible veterans, service members, guard, reservist, and family members under the transfer of entitlement provisions. The responsibility of determining basic eligibility for Chapter 1606 is placed on the DoD. The responsibility of determining basic eligibility for Chapter 30 and Chapter 33 is placed on VA, while the responsibility of providing initial eligibility data for Chapter 30 and Chapter 33 is placed on DoD. Thus, the two agencies must exchange data to ensure that VA makes payments only to those who are eligible for a program. Without an exchange of enrollment and eligibility data, VA would not be able to establish or verify applicant and recipient eligibility for the programs. Subject to the due process requirements, set forth in Article VII.B.1., 38 U.S.C. §3684A, VA may suspend, terminate, or make a final denial of any financial assistance on the basis of data produced by a computer matching program with DoD. To minimize administrative costs of implementation of the law and to maximize the service to the veteran or service member, a system of data exchanges and subsequent computer matching programs was developed. The purposes of the computer matching programs are to minimize the costs of administering the Xxxxxxxxxx GI Xxxx — Active Duty, the Xxxxxxxxxx GI Xxxx — Selected Reserve, Reserve Educational Assistance Program, and the Post-9/11 GI Xxxx program; facilitate accurate payment to eligible veterans or service members training under the Chapter of the Xxxxxxxxxx GI Xxxx — Active Duty, the Xxxxxxxxxx GI Xxxx — Selected Reserve, Reserve Educational Assistance Program, and the Post-9/11 GI Xxxx program; and to avoid payment to those who lose eligibility. The current automated systems, both at VA and DoD, have been developed over the last twenty-two years. The systems were specifically designed to utilize computer matching in transferring enrollment and eligibility data to facilitate accurate payments and avoid incorrect payments. The source agency, DMDC, stores eligibility data on its computer based system of record. The cost of providing this data to VA electronically are minimal when compared to the cost DMDC would incur if the data were forwarded to VA in a hard-copy manner. By comparing records electronically, VA avoids the personnel costs of inputting data manually as well as the storage costs of the DMDC documents. This results in a VA estimated annual savings of $26,724,091 to VA in mailing and data entry costs. DoD reported an estimated annual savings of $12,350,000. A cost-benefit analysis is at Attachment 1. In the 32 years since the inception of the Chapter 30 program, the cost savings of using computer matching to administer the benefit payments for these educational assistance programs have remained significant. The implementation of Chapter 33 has impacted the Chapter 30 program over the past 8 years (fiscal year 2010 through fiscal year 2017). Statistics show a decrease of 23 percent in the number of persons who ultimately use Chapter 30 from fiscal year 2015 to 2016. The number of persons who use Chapter 33 has consistently been above 700,000 in the past four years. VA foresees continued cost savings due to the number of persons eligible for the education programs.
Results and Discussion Table 1 (top) shows the root mean square error (RMSE) between the three tests for different numbers of topics. These results show that all three tests largely agree with each other but as the sample size (number of topics) decreases, the agreement decreases. In line with the results found for 50 topics, the randomization and bootstrap tests agree more with the t-test than with each other. We looked at pairwise scatterplots of the three tests at the different topic sizes. While there is some disagreement among the tests at large p-values, i.e. those greater than 0.5, none of the tests would predict such a run pair to have a significant difference. More interesting to us is the behavior of the tests for run pairs with lower p-values. ≥ Table 1 (bottom) shows the RMSE among the three tests for run pairs that all three tests agreed had a p-value greater than 0.0001 and less than 0.5. In contrast to all pairs with p-values 0.0001 (Table 1 top), these run pairs are of more importance to the IR researcher since they are the runs that require a statistical test to judge the significance of the per- formance difference. For these run pairs, the randomization and t tests are much more in agreement with each other than the bootstrap is with either of the other two tests. Looking at scatterplots, we found that the bootstrap tracks the t-test very well but shows a systematic bias to produce p-values smaller than the t-test. As the number of topics de- creases, this bias becomes more pronounced. Figure 1 shows a pairwise scatterplot of the three tests when the number of topics is 10. The randomization test also tends to produce smaller p-values than the t-test for run pairs where the t- test estimated a p-value smaller than 0.1, but at the same time, produces some p-values greater than the t-test’s. As Figure 1 shows, the bootstrap consistently gives smaller p- values than the t-test for these smaller p-values. While the bootstrap and the randomization test disagree with each other more than with the t-test, Figure 1 shows that for a low number of topics, the randomization test shows less noise in its agreement with the bootstrap com- Figure 1: A pairwise comparison of the p-values less than 0.25 produced by the randomization, t-test, and the bootstrap tests for pairs of TREC runs with only 10 topics. The small number of topics high- lights the differences between the three tests. pared to the t-test for small p-values.
Audit Results If an audit by a Party determines that an overpayment or an underpayment has occurred, a notice of such overpayment or underpayment shall be given to the other Party together with those records from the audit which support such determination.
Financial Conditions Section 4.01. (a) The Recipient shall maintain or cause to be maintained a financial management system, including records and accounts, and prepare financial statements in a format acceptable to the Bank, adequate to reflect the operations, resources and expenditures in respect of the Project and each Sub-project (including its cost and the benefits to be derived from it).
Test Results The employer, upon request from an employee or former employee, will provide the confidential written report issued pursuant to 4.9 of the Canadian Model in respect to that employee or former employee.
DATA COLLECTION AND ANALYSIS The goal of this task is to collect operational data from the project, to analyze that data for economic and environmental impacts, and to include the data and analysis in the Final Report. Formulas will be provided for calculations. A Final Report data collection template will be provided by the Energy Commission. The Recipient shall: • Develop data collection test plan. • Troubleshoot any issues identified. • Collect data, information, and analysis and develop a Final Report which includes: o Total gross project costs. o Length of time from award of bus(es) to project completion. o Fuel usage before and after the project.
Narrative Results i. A description of Xxxxxxx’x billing and coding system(s), including the identification, by position description, of the personnel involved in coding and billing.
