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. 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 listed 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 group’s individual scores Detailed results of this sensitivity analysis can be found in the complete Option Appraisal report if required.
Sensitivity Analysis. In conducting a sensitivity analysis, the CONSULTANT will evaluate the sensitivities of different asset classes (e.g., transportation assets, infrastructure, community/emergency facilities, and natural/cultural/historical resources). The objective will be to evaluate the impacts projected to be incurred to each critical asset class and to assign sensitivity ratings based on impact severity.
Sensitivity Analysis. After the steady state (predevelopment or pre-desalination conditions for density flow models) and transient models are calibrated, a sensitivity analysis on each major parameter in the model shall be performed (see, for example, Xxxx and others, 2000; Xxxxxxxx and Xxxxxxxx, 1992, Figure 8.15). Sensitivity analysis quantifies the uncertainty of the calibrated model to the uncertainty in the estimates of aquifer parameters, stresses, and boundary conditions (Xxxxxxxx and Xxxxxxxx, 1992, p. 246) and is an essential step in modeling (Freeze and others, 1990). Sensitivity analysis assesses the adequacy of the model with respect to its intended purpose (ASTM, 1994) and can offer insights into the non-uniqueness of the calibrated model. Sensitivity analysis also identifies which hydrologic parameters most influence changes in water levels, flows to springs, streams, and rivers, and can identify parameters that justify additional future study. Sensitivity analysis shall be performed by globally adjusting each model parameter and assessing its impact on water levels and fluxes (for example, spring flow, base flow, and cross-formational flow). Model parameters include: Horizontal hydraulic conductivity, Vertical hydraulic conductivity, Confined storativity, Specific yield, Recharge, Pumping, Hydraulic head assigned at any constant head and general head boundaries, Conductance values for drains, rivers, general head boundaries, or any other packages for each layer, Hydrodynamic dispersion through dispersivity values (density flow models), Initial total dissolved solids values (density flow models), Boundary conditions for transport models, and Grid discretization or an explanation of how it is optimized and does not have a significant impact on model results.
Sensitivity Analysis. See Appendix A Section 3.4.
Sensitivity Analysis. Consider the log-linear model after log transformation of the Armitage-Doll multistage model log(ri) = α + (s − 1) log(ti) + ϵi, i = 1, ..., n, where log(ri) is the cancer mortality rate for age group i, log(ti) is the median age for age group i and the ϵi are independent random errors having density f with mean 0. Xxxxxxx and Xxxxxxx [1974] proved that defining the distribution of the error term to ϵi|σ2, λi ∼ N (0, λiσ2), i = 1, ..., n, and putting a prior on λi can incorporate various familiar and more widely dispersed error densities. In other words, the scale mixture of normal densities is created as follows: f (ϵi|σ2) = ∫ p(ϵi|σ2, λi)p(λi)dλi, i = 1, ..., n. The following list gives different formats for p(λi) to obtain non-normal distributions. Student’s t errors: λi ∼ IG(ν/2, 2/ν); Double exponential errors: λi ∼ expo(2); Logistic errors: if 1/√λi has the asymptotic Kolmogorov distance distribution, then ϵi|σ2 is logistic. Due to the uncertainty about the error density and the impact of possible outliers, three different error densities are compared: ϵi ∼ N (0, σ2); ϵi ∼ t(0, σ2, ν = 2); ϵi ∼ double expo(0, σ)). Sensitivity analysis is implemented on the Bayesian Armitage-Doll model for colon, esopha- xxx, breast and lung cancer, as shown in Table 3.5. In colon cancer, the parameter s is more precisely estimated in the nonnormal errors (t and double exponential distribution) than in the normal errors, as indicated by the smaller standard deviations and narrower HPDs. The heavier tails in the t and double exponential errors can dissolve the negative effects caused by the possible large outliers more quickly than the normal errors. Therefore, the posterior estimates for the parameter s can be achieved with higher accuracy and efficiency [Xxxxxx and Xxxxx, 2009]. In esophagus, breast and lung cancer, we observe the disturbance in the posterior estimate for the number of stage s as we change the prior assumptions for the model error terms. The posterior estimate in esophagus cancer is apparently in- accurate when t errors are defined. Furthermore, the standard deviations obtained in the t errors cases for breast and lung cancer are even higher than those in the normal errors cases, showing that it is not efficient in the posterior estimate as the error terms change from normal to t distribution. However, the performance of double exponential errors in esophagus, breast and lung cancer is satisfactory with improved efficiency and accuracy in obtaining t...
Sensitivity Analysis. The slightest of changes in conditions can have a detrimental impact on cash flows. Given the initial assumptions, however, if revenue is 5% less than expected while expenses rise by 5%, the following situation arises: DISCOUNTED CASH FLOWS $800 $700 $600 $500 $400 $300 $200 $100 $- $(100) 1 2 3 YEARS 4 5 105.41% Expense/Revenue 94.28% 84.84% 74.69% 66.42% 2 3 Year 4 5 CASH FLOWS E/R (%) THOUSANDS We can breakeven in Year 2 and gradually progress throughout the remaining forecast period in an adverse situation. We are more than capable of maintaining a positive cash flow position and retaining healthy profits in the years to come. The net present value is positive, indicating that the company is a beneficial investment. This demonstrates the viability of the business model and its propensity to perform even when performance is lower than expected. The expense/revenue is expected to be higher in this situation. However, much like the first scenario, the company can push down costs and earn better margins. Sensitivity Analysis $1,200 $1,000 $800 $600 $400 $200 $- 1 2 3 4 5 $(200) Year Worst Case Scenario Expected Scenario Net Cash Flow Thousands
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. Sensitivity analysis is also an approach to deal with uncertainty and complexity. NPV is determined through estimating of the cash flows, depending on different variables. Sensitivity analysis is the process to observe the key primary variables, which may affect upon NPV. In others words, the process is to change one key primary variable each time and keep others the same, then identify the result of NPV. This approach gives a picture of the possible variation in or sensitivity of NPV when a given risky variable is wrong estimated. There is a possibility that a variable itself maybe very risky, but it has small affected overall project’s NPV. On the contrary, a non-risky variable may have a big impact on the whole project’s NPV. It is easy to find how large forecast errors of a variable through this analysis before making a decision of investment. However, sensitivity analysis still has its limitations. First, it only considers the impact on NPV of one variable each time and ignores the misestimates of more than one variable together at the same time. Second, if there are dependences among all the variables, it is meaningless to examine them in isolation (Xxxxxxxxxx Xxxxx, 1996). This means that one variable may influence to another one. In the petroleum industry, there are some connections among transportation risks, price volatility, and technical issues. In fact, these factors may have effects to the whole project at the same time because they are correlated. For example, the oil companies could not transport gas on time because of the technical issues. Therefore, the customers may not get the enough quantity of gas what they want. In this situation, the demand of gas may lead to exceed supply and this may result in to increase gas price volatility. Therefore, once one variable is changed, the other variables could be changed because of inherent dependences and this could have significant impact on NPV. Because these variables are not independent, the accuracy of one variable’s estimate depends on another variable. When only focusing one variable each time, the result of NPV may have no difference. It makes no sense to analyze these variables separately. In addition, because of false estimates of a variable, forecast error in one year may generate higher errors in the following several years that may result in greater impact on NPV.
