Sensitivity Analysis Sample Clauses
A Sensitivity Analysis clause requires the parties to assess how changes in key variables or assumptions could impact the outcomes of a contract or financial model. In practice, this involves modeling different scenarios—such as fluctuations in interest rates, costs, or market demand—to understand the range of possible results and associated risks. The core function of this clause is to promote informed decision-making by highlighting potential vulnerabilities and ensuring that both parties are aware of how sensitive the agreement is to various factors.
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Sensitivity Analysis. As discussed, there is a degree of uncertainty associated with the models and data used to devise Australia’s FM reference level. Due to this, the Government’s estimates of emissions and removals from native forests are subject to a significant margin of error and, as the method used here is a replica of the Australian Government’s, it embodies all of the same uncertainties. To account for this, and the potential for future modifications of the method and data sets to alter the FM credit outcomes, sensitivity analysis was undertaken by changing two of the key parameters in FullCAM: the above-ground live biomass yield increment rates and the age-class distribution of the forests subject to harvest. The margin of error associated with the above-ground live biomass yield increment rates was assumed to be ±25%. To account for this range, replica representative plot files were created with +25% and -25% yield increments. The reference and ENGO scenarios were then re-run to test how the lower and higher yield increments affected the credit outcomes. In relation to the uncertainties associated with the age-class distribution of the forests, the estate simulation start date was adjusted ±10 years. In the standard runs, the estate simulation start date was 1 January 1960, meaning that in the sensitivity analysis the simulation start dates were 1 January 1950 and 1 January 1970.
Sensitivity Analysis. A summary of the discounted cash flow results from -------------------- varying key assumptions (such as the discount rate, commodity pricing and/or major operating assumptions); and
Sensitivity Analysis. Description: The Grantee will perform the sensitivity analysis to measure the impact of flooding on assets and to apply the data from the exposure analysis to the inventory of critical assets created in the Exposure Analysis Task. The sensitivity analysis should include an evaluation of the impact of flood severity on each asset type and at each flood scenario and assign a risk level based on percentages of land area inundated and number of critical assets affected. Deliverables: The Grantee will provide the following:
Sensitivity Analysis. In the event of an economic downturn, the business may have a decline in its revenues. Hobby products and supplies are not necessities, and an economic recession may have an impact on the Company’s ability to generate sales as consumers have less discretionary income. However, the business will be able to remain profitable and cash flow positive given its high margins from both retail and online sales.
Sensitivity Analysis. In order to explore the potential impact of a range of variances on the numerical outputs from the option appraisal process, a limited sampling-based sensitivity analysis was conducted. This attempted to understand the main effects of varying key values on the relative prioritisation and scoring of options. The sensitivity analysis conducted considered the variables below: Variable 1: Applying overall (group) scores to amended weightings based on the inclusion / exclusion of the weighting identified by individual stakeholder groups Variable 2: Applying individual stakeholder group scores to agreed overall weightings Variable 3: Excluding single individual stakeholder group scores from agreed overall scores and weightings (using an amended mean score) Variable 4: Applying individual group weightings to the same groups individual scores
Sensitivity Analysis. Analyses on the PP population will be performed for the primary and the secondary efficacy endpoints and serve as supportive analyses for the ITT analyses.
Sensitivity Analysis. Risks and uncertainties inherent in the hydrologic modeling procedures involve methodology selection for each of the components of the hydrologic process, parameter estimation, and the model-building process. In order to evaluate the impact of uncertainties inherent in the hydrologic input parameters on peak flows and volumes, a parameter sensitivity analysis will be conducted on the following major hydrologic input variables: • Retention storage within the watershed, specifically within the area of south of ▇▇▇▇▇ ▇▇. (▇▇▇▇▇ Wash) and east of SR 347; • Retention storage within agricultural fields and by major secondary irrigation canals; • Flood storage routing/attenuation by the CAP Canal embankments and through the overchutes; • ▇▇▇▇▇▇▇’▇ “n” values for the sheet flow routing conditions (most of the routing depth around 1.5 feet); • Potential flow diversions out of the watershed from areas near I-8 southeast of the watershed. Flow routing transmission loss is significant for this watershed. However, it will not be included in the hydrology refinement study and will be treated as a safety factor.
Sensitivity Analysis. The increase / (decrease) in the present value of defined benefit obligations as a result of change in each assumption, keeping all other assumptions constant: 1% increase in discount rate 134,676 136,075 1% decrease in discount rate 159,570 161,101 1 % increase in expected rate of salary increase 160,148 161,660 1 % decrease in expected rate of salary increase 133,983 135,387
Sensitivity Analysis. The Company’s revenues are somewhat sensitive to the overall conditions of the economy. During times of economic recession, the Company may have a decrease in its top line revenues as people will demand fewer beverages/food products from retail locations. However, the Company’s revenues provide high levels of operating income for the business, and the Auntie ▇▇▇▇’s franchise would need to have a significant decrease in its top line income before the Company becomes unprofitable.
Sensitivity Analysis. We are here concerned generally with lack of certainty about parameters such as rates but even in the case where rate information can be known with high confidence the framework which we have available for performing a parameter sweep across the rate constants can be used to perform sensitivity analysis. One way in which the results obtained by sensitivity analysis can be used is to determine which activities of the system are bottlenecks. That is, to discover which rate or rates should we alter to ensure that the user sees the greatest improvement in performance. We have an evaluation function which assigns a score to each solution of the underlying Markov chain. In this case, the less is the response time then the higher is the score. It might seem that the results obtained from sensitivity analysis are likely to be pretty unsurprising and that it will turn out to be the case that increasing the .4 1 0.8 0.6 0.4 0.2 0 1 .9 0.9 0.8 .7 0.7 .6 0.6 .5 0.5 0.4 .3 0.3 .2 0.2 1 0.8 0.6 0.4 0.2 0 .1 0.1 7 8 9 10 7 8 9 10 0.45 0.5 0.55 5 6 0.45 0.5 0.55 5 6 main__LMU__avail 0.6 0.65 0.7 0.75 0.8 0.85 0.91 2 3 4 time main__LMU__avail 0.6 0.65 0.7 0.75 time 0.8 0.85 0.91 2 3 4 0 0 0 0 0 0 0 (Results of PEPA model 1) (Results of PEPA model 3)
Fig. 1. Graphs showing sensitivity analysis over the rates in the produced models. The basic plot is a cumulative distribution function showing how the probability of completion of the uploads and downloads increases as a function of time. The surface plot is obtained from this because we vary one of the parameters. Here in both cases we vary the availability of the Munich server from 50% availability to 90% availability. Expressed as a scaling factor this becomes 0.5 to 0.9. rate of any activity brings about a proportional decrease in response time. To see that this is not the case, we will compare two sets of results. Recall that SRMC generates many PEPA models; we number these. The first set of results shown in Fig. 1 comes from PEPA model 1, where Edinburgh is the upload portal, Mu- nich the download portal, and ▇▇▇▇▇ is the client. In model 3 they swap around so that Munich is the upload portal, Edinburgh the download, and ▇▇▇▇▇ is again the client. In the latter case low availability of the Munich server makes a no- ticeable impact on response time (the curve takes longer to get up to 1) but in the former case the low availability of the Munich server has negligible impact. This is made clear in the results but it is unlike...