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. 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. 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 the event of a severe economic decline, the demand for specialty online based advertising may decrease significantly, which may cause the revenue generated by the business to level off or decline. However, Google AdSense and internal online advertising programs have proven to be a very popular advertising method for many businesses, and only in a steep economic decline does Management foresee a decline in top line revenues.
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.
Sensitivity Analysis. Reliability analysis is used to evaluate a given design. If the reliability analysis result shows that the reliability is not satisfactory, i.e., lower than the required reliability, there are several ways to improve the reliability. Some of them are
(1) To change the mean values of the random variables,
(2) To reduce the variances of the random variables, and
(3) To truncate the distributions of the random variables. When the number of random variables is large, it is difficult to change the distributions of all the random variables. It is also not economic to control all the random variables. To effectively improve the design, a question of interest is: For which random variables we should make changes to order to improve reliability? To answer this question, we need to perform sensitivity analysis. With the information of sensitivity, we will be able to identify the most significant random variables. Only the important variables need to be managed. The sensitivity analysis can give us right directions for the improvement. Reliability sensitivity analysis is used to find the rate of change in the probability of failure (or reliability) due to the changes in the parameters (usually mean and standard deviation) of distributions. For a distribution parameter p of random variable sensitivity is defined by Xi , the s = ∂pf p ∂p s p can be derived as follows. s = ∂pf = ∂Φ(− b ) = ∂Φ(−b ) ∂b = −ƒ (−b ) ∂b p ∂p ∂p ∂b ∂p ∂p The derivative of the reliability index with respect to the distribution parameter is given by ∂b ∂b ∂u* = i ∂p ∂u* ∂p where ∂ ∑ j=1 ( ) * 2 ∂b u* u* ∑ ( ) * 2 j=1 = = = i ∂u* ∂u* b
Sensitivity Analysis. By applying three emission inventories in the present year, four future scenarios in the projected year 2050, one C-R function and eight population projections, we calculated 96 health outcome estimations for the year 2050. Uncertainties exist at each input step. The sensitivity analysis reflects the variations from different factors in health impact estimation (Xxxxxxxx at al., 2010). In addition, we also considered the 95% CI of our C-R function, which contributed to more uncertainties in the health estimation. In order to quantify how much the input factor Xi affects the distribution of health outcome Hi, we used a sensitivity analysis approach using ANOVA decomposition method given the ith input to describe the pattern of the model H=f(1, 2, … ). The total variance of H can be decomposed to the partial variance that contributed by the total k factors (H|1, H|2, … H|) and their interactions (H| 1, 2, H| 1, 3, H| 2, 3, … H| 1, 2, 3…) (Xxxxxxxx at al., 2010). The decomposition of the total variance, V, is based on: = (|1) + (|2) + ⋯ + (|1, 2, … )
(3-a) The associated sensitivity measure Si, which is a first order sensitivity coefficient, is given by: = (|) (3-b) Si is our sensitivity indices to assess how much variance each input contributed to the total variability. A larger Si indicates larger contribution and vice visa.
Sensitivity Analysis. To assess the robustness of the final model and the potential impact of the lack of an HIV diagnosis on mortality, a sensitivity analysis was performed with an updated HIV status variable that redefined those listed as dying of HIV as HIV infected.
