Sensitivity analysis guide
Sensitivity analysis dcf
Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. Authored by the leading authorities on sensitivity analysis. Sensitivity analysis can be used for optimal experimental design , e. The emphasis on the framing may derive inter-alia from the relevance of the policy study to different constituencies that are characterized by different norms and values, and hence by a different story about 'what the problem is' and foremost about 'who is telling the story'. It is focussed on the use of SIMLAB — a widely distributed freely-available sensitivity analysis software package developed by the authors — for solving problems in sensitivity analysis of statistical models. In sensitivity analysis one looks at the effect of varying the inputs of a mathematical model on the output of the model itself. In a design of experiments, one studies the effect of some process or intervention the 'treatment' on some objects the 'experimental units'. Chemistry[ edit ] Sensitivity analysis is common in many areas of physics and chemistry. However, this may not be the case. Sensitivity analysis can also be used to identify the redundant species and reactions allowing model reduction. Moreover, computer models are increasingly used for environmental decision-making at a local scale, for example for assessing the impact of a waste water treatment plant on a river flow, or for assessing the behavior and life-length of bio-filters for contaminated waste water.
Time-critical decision making[ edit ] See also: Window of opportunity Producing time-critical accurate knowledge about the state of a system effect under computational and data acquisition cause constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake, e.
It is important to distinguish here the notion of criticality with that of importance. Often the results are surprising, lead to finding problems in the data or model development, and fixing the problems. Table of Contents.
Saltelli sensitivity analysis
Opens with a detailed worked example to explain the motivation behind the book. Sensitivity analysis may be used to identify which set of input data signals has a significant impact on the set of system state information i. For instance, the field of multi-criteria decision making MCDM studies among other topics the problem of how to select the best alternative among a number of competing alternatives. Social sciences[ edit ] Sensitivity analysis is common practice in social sciences. He is also the main author and main editor of two previous books both for Wiley. It is important to distinguish here the notion of criticality with that of importance. In both disciplines one strives to obtain information from the system with a minimum of physical or numerical experiments. A famous early example is Mroz , who analysed econometric models of female labor market participation. Through a cause-effect analysis technique, sensitivity can be used to support the filtering of unsolicited data to reduce the communication and computational capabilities of a standard supervisory control and data acquisition system.
See Corporate finance: Quantifying uncertainty. Sensitivity analysis can also be used to identify the redundant species and reactions allowing model reduction.
In sensitivity analysis one looks at the effect of varying the inputs of a mathematical model on the output of the model itself. In a design of experiments, one studies the effect of some process or intervention the 'treatment' on some objects the 'experimental units'.
For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. Chemistry[ edit ] Sensitivity analysis is common in many areas of physics and chemistry. In such a setting each alternative is described in terms of a set of evaluative criteria.
Sensitivity analysis excel
In these cases, depending on model complexity, different sampling strategies may be advisable and traditional sensitivity indices have to be generalized to cover multiple model outputs,  heteroskedastic effects and correlated inputs. This is an important task in decision making. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. If results are consistent it provides stronger evidence of an effect and of generalizability. Therefore, the most limiting step can be identified and the cell state for most advantageous scale-up and expansion can be determined. Additionally to the general motivations listed above, sensitivity analysis can help in a variety of other circumstances specific to business: To identify critical assumptions or compare alternative model structures To guide future data collections To optimize the tolerance of manufactured parts in terms of the uncertainty in the parameters To optimize resources allocation However, there are also some problems associated with sensitivity analysis in the business context: Variables are often interdependent correlated , which makes examining each variable individually unrealistic. Intuitively, one may think that the larger the weight for a criterion is, the more critical that criterion should be. This, in turn, may dramatically improve the effectiveness of the initial study and assist in the successful implementation of the final solution. It is possible criteria with rather small weights of importance i. In a design of experiments, one studies the effect of some process or intervention the 'treatment' on some objects the 'experimental units'.
However, this may not be the case.
based on 75 review