Sensitive Analysis
Sensitive Analysis - Related Videos
09:14
Paw-Dragging: a Novel, Sensitive Analysis of the Mouse Cylinder Test
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Cited by 53 •
2015
Classical forelimb asymmetry analysis of the cylinder test is routinely used to assess behavioural deficits in rats following brain injury or stroke; however, it fails to detect consistent deficits in mice. This study demonstrates that quantifying paw-dragging behaviour is a more sensitive analysis of brain injury in mice.
01:29
Using Sensitivity Analysis to Optimize Profits
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2026
Sensitivity analysis is a vital tool in managerial decision-making, particularly when evaluating how changes in key business variables such as costs, prices, or sales volumes affect overall profitability. This technique provides managers with a systematic approach to assess the financial implications of alternative strategies and external uncertainties.Sensitivity analysis involves altering one or more variables in a financial model to measure the impact on key outcomes, such as net income or...
10:18
Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials
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Cited by 8 •
2019
A method for exfoliating large thin flakes of air sensitive two-dimensional materials and safely transporting them for analysis outside of a glovebox is presented.
10:30
Dye-sensitized Solar Cells
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2023
Source: Tamara M. Powers, Department of Chemistry, Texas A&M University Today's modern world requires the use of a large amount of energy. While we harness energy from fossil fuels such as coal and oil, these sources are nonrenewable and thus the supply is limited. To maintain our global lifestyle, we must extract energy from renewable sources. The most promising renewable source, in terms of abundance, is the sun, which provides us with more than enough solar energy to fully fuel our...
14:04
Neutrophil Isolation and Analysis to Determine their Role in Lymphoma Cell Sensitivity to Therapeutic Agents
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Cited by 11 •
2016
Neutrophils are the most abundant type of white blood cells. The isolation of neutrophils from human blood by density gradient separation method and the differentiation of human promyelocytic (HL60) cells along the granulocytic pathway are described here; to test their role on sensitivity of lymphoma cells to anti-lymphoma agents.
FAQs
Sensitivity analysis identifies influential variables by relating deliberate input changes to changes in a model’s output, prediction, or performance. Variables that produce larger output changes deserve closer attention than variables with little apparent effect. This ranking helps engineers prioritize parameters, focus uncertainty assessment, and judge whether conclusions remain robust when inputs vary.
Varying one parameter while holding the others constant makes the resulting output change easier to associate with that input. Engineers can therefore examine individual influences without simultaneously changing every condition. The approach is useful for diagnosing model behavior and deciding whether a particular parameter deserves improved measurement or further investigation.
When several parameters may vary, the analysis can examine their effects on system predictions or performance rather than relying on a single fixed scenario. This helps expose how uncertainty in inputs may affect engineering conclusions. The resulting evidence supports robustness evaluation, risk assessment, and decisions about where added data or experimental testing could provide the greatest benefit.
An engineering workflow begins by selecting the model inputs and output or performance measure of interest. Engineers then vary relevant parameters, keep other conditions fixed when isolating an input, and compare the resulting outputs or predictions. The comparison identifies influential variables and indicates which measurements, data, or tests warrant priority.
Applications extend across structures, energy systems, manufacturing, and control engineering. In each setting, engineers can use the results to determine which parameters most affect predicted behavior or performance. That information helps concentrate analysis and measurement effort on the parts of the system most relevant to design, performance evaluation, and uncertainty assessment.
Design teams can use sensitivity results to identify which inputs deserve attention during optimization and which uncertainties pose greater decision risk. The same evidence can guide resource allocation by directing measurement, modeling, or testing effort toward influential parameters. As a result, engineering decisions can rely on targeted improvements rather than treating every input equally.