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.
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 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).
Settlement Discussions This Agreement is part of a proposed settlement of matters that could otherwise be the subject of litigation among the Parties hereto. Nothing herein shall be deemed an admission of any kind. Pursuant to Federal Rule of Evidence 408 and any applicable state rules of evidence, this Agreement and all negotiations relating thereto shall not be admissible into evidence in any proceeding other than to prove the existence of this Agreement or in a proceeding to enforce the terms of this Agreement.
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.
Budget Narrative Services are strictly paid as cost reimbursement. No funds will be paid for services not provided.
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.
