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A what if chart (Whif chart, WHIF analysis, etc.) is a visual tool for modeling the outcome of a combination of different factors.The table can represent actual results or predicted outcome based on combinations of parameters.
The structured what-if technique (SWIFT) is a prospective hazards analysis method that uses structured brainstorming with guidewords and prompts to identify risks, [1] with the aim of being quicker than more intensive methods like failure mode and effects analysis (FMEA). [2] [3] It is used in various settings, including healthcare. [1] [2] [3] [4]
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For example, a comparison of a spreadsheet cost construction model run using traditional "what if" scenarios, and then running the comparison again with Monte Carlo simulation and triangular probability distributions shows that the Monte Carlo analysis has a narrower range than the "what if" analysis.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.
Sequential analysis also has a connection to the problem of gambler's ruin that has been studied by, among others, Huygens in 1657. [12] Step detection is the process of finding abrupt changes in the mean level of a time series or signal. It is usually considered as a special kind of statistical method known as change point detection. Often ...
Statistics subsequently branched out into various directions, including decision theory, Bayesian statistics, exploratory data analysis, robust statistics, and non-parametric statistics. Neyman-Pearson hypothesis testing made significant contributions to decision theory, which is widely employed, particularly in statistical quality control.
Meta-analysis: Though independent p-values can be combined using Fisher's method, techniques are still being developed to handle the case of dependent p-values. Behrens–Fisher problem : Yuri Linnik showed in 1966 that there is no uniformly most powerful test for the difference of two means when the variances are unknown and possibly unequal.