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Multiple-criteria decision-making ( MCDM) or multiple-criteria decision analysis ( MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). It is also known as multiple attribute utility theory ...
Analytic hierarchy process. A simple AHP hierarchy, with final priorities. The goal is to select the most suitable leader from a field of three candidates. The factors to be considered are experience, education, charisma, and age. According to the judgments of the decision makers, Dick is the strongest candidate, followed by Tom, then Harry.
Dunnett's test's calculation is a procedure that is based on calculating confidence statements about the true or the expected values of the differences , thus the differences between treatment groups' mean and control group's mean. This procedure ensures that the probability of all statements being simultaneously correct is equal to a specified ...
Multiple comparisons problem. An example of coincidence produced by data dredging (uncorrected multiple comparisons) showing a correlation between the number of letters in a spelling bee's winning word and the number of people in the United States killed by venomous spiders. Given a large enough pool of variables for the same time period, it is ...
Building a vehicle comparison spreadsheet is super helpful for processing the mountains of data you should be compiling if doing your due diligence when shopping for a new or used car. But if you ...
Weighted sum model. In decision theory, the weighted sum model ( WSM ), [1] [2] also called weighted linear combination ( WLC) [3] or simple additive weighting ( SAW ), [4] is the best known and simplest multi-criteria decision analysis (MCDA) / multi-criteria decision making method for evaluating a number of alternatives in terms of a number ...
t. e. In statistics, the Bayesian information criterion ( BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among a finite set of models; models with lower BIC are generally preferred. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).
In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance.Notionally, any F-test can be regarded as a comparison of two variances, but the specific case being discussed in this article is that of two populations, where the test statistic used is the ratio of two sample variances. [1]
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