Contingency Analysis
Contingency Analysis - Related Videos
01:29
Contingency Table
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2023
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
01:20
Contingent Liabilities
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2025
Companies often face uncertain situations that could result in future financial obligations. These are known as contingent liabilities—potential debts dependent on the outcome of future events. While not always recorded on the balance sheet, contingent liabilities are essential for financial transparency and risk management.Recognition and Disclosure CriteriaContingent liabilities are recognized only if the obligation is probable and the amount can be reasonably estimated. If either condition...
07:12
A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
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Cited by 1 •
2025
We present development of a gaze-contingent display framework designed for perceptual and oculomotor research simulating central vision loss. This framework is particularly adaptable for studying compensatory behavioral and oculomotor strategies in individuals experiencing both simulated and pathological central vision loss.
09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Cited by 6 •
2017
We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
FAQs
N-1 analysis tests whether a network remains within its safety, reliability, and design limits after losing one transmission line or generator. This single-failure case gives engineers a consistent resilience check without waiting for an actual disruption. A failed test identifies conditions requiring corrective action, preventive maintenance, or further design attention.
Engineers can remove a component, change a load, or introduce another credible event in the model. These alterations represent different stresses on system performance, allowing each scenario to be compared with established safety, reliability, and design limits. Varying the modeled condition helps identify which hypothetical events could produce unacceptable operating results.
Interpretation centers on the difference between calculated performance and the applicable safety, reliability, and design limits. A scenario that remains within those limits indicates acceptable modeled operation for that case, while an adverse result signals a vulnerability. Engineers can then prioritize corrective actions and preventive maintenance according to the seriousness of the finding.
First, engineers select a hypothetical failure, disruption, or operating change that the model should represent. They then modify the system model, calculate the resulting performance, and compare the result with relevant limits. Finally, they use the findings to identify vulnerabilities, prioritize corrective actions, guide preventive maintenance, or improve the resilience of the infrastructure.
Engineers use this approach to examine credible disruptions before they occur, including the loss of a transmission line or generator in a power network. Modeling the event supports proactive decisions about maintenance and corrective measures. It is especially useful for complex infrastructure where an actual failure could expose vulnerabilities only after system performance has already been affected.
The results can expose vulnerabilities, prioritize corrective actions, and inform preventive-maintenance decisions. They also support improvements in system resilience by showing how modeled changes affect performance relative to safety, reliability, and design limits. Across complex infrastructure, this connection between hypothetical events and performance consequences helps engineers make better-informed decisions before disruptions occur.