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  2. Potentially all pairwise rankings of all possible alternatives

    en.wikipedia.org/wiki/Potentially_all_pairwise...

    The PAPRIKA method can be easily demonstrated via the simple example of determining the point values (weights) on the criteria for a value model with just three criteria – denoted by 'a', 'b' and 'c' – and two categories within each criterion – '1' and '2', where 2 is the higher ranked category.

  3. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  4. Multiple-criteria decision analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple-criteria_decision...

    In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients. 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).

  5. Weighted product model - Wikipedia

    en.wikipedia.org/wiki/Weighted_product_model

    The weighted product model (WPM) is a popular multi-criteria decision analysis (MCDA) / multi-criteria decision making (MCDM) method. It is similar to the weighted sum model (WSM) in that it produces a simple score, but has the very important advantage of overcoming the issue of 'adding apples and pears' i.e. adding together quantities measured in different units.

  6. Bayesian information criterion - Wikipedia

    en.wikipedia.org/wiki/Bayesian_information_criterion

    ^ = the maximized value of the likelihood function of the model , i.e. ^ = (^,), where {^} are the parameter values that maximize the likelihood function and is the observed data; n {\displaystyle n} = the number of data points in x {\displaystyle x} , the number of observations , or equivalently, the sample size;

  7. TOPSIS - Wikipedia

    en.wikipedia.org/wiki/TOPSIS

    The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 [1] with further developments by Yoon in 1987, [2] and Hwang, Lai and Liu in 1993. [3]