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  2. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    When computing a t-test, it is important to keep in mind the degrees of freedom, which will depend on the level of the predictor (e.g., level 1 predictor or level 2 predictor). [5] For a level 1 predictor, the degrees of freedom are based on the number of level 1 predictors, the number of groups and the number of individual observations.

  3. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the uncertainty that is present. The result of this integration is the posterior distribution, also known as the updated probability estimate, as additional evidence on the prior distribution is acquired.

  4. Hierarchical generalized linear model - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_generalized...

    For example, this method was used to analyze semiconductor manufacturing, because interrelated processes form a complex hierarchy. [6] Semiconductor fabrication is a complex process which requires different interrelated processes. [7] Hierarchical generalized linear model, requiring clustered data, is able to deal with complicated process.

  5. Analytic network process - Wikipedia

    en.wikipedia.org/wiki/Analytic_network_process

    The analytic network process (ANP) is a more general form of the analytic hierarchy process (AHP) used in multi-criteria decision analysis. AHP structures a decision problem into a hierarchy with a goal, decision criteria, and alternatives, while the ANP structures it as a network.

  6. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    The recursion is completed when the subset at a node has all the same values of the target variable, or when splitting no longer adds value to the predictions. This process of top-down induction of decision trees (TDIDT) [5] is an example of a greedy algorithm, and it is by far the most common strategy for learning decision trees from data. [6]

  7. Analytic hierarchy process - Wikipedia

    en.wikipedia.org/wiki/Analytic_hierarchy_process

    The priorities of the alternatives always add up to 1.000. Things can become complicated with multiple levels of Criteria, but if there is only one level, their priorities also add to 1.000. All this is illustrated by the priorities in the example below. Simple AHP hierarchy with associated default priorities

  8. NFC championship game prediction: Who will win Eagles vs ...

    www.aol.com/nfc-championship-game-prediction-win...

    NFC championship game prediction (2) Philadelphia Eagles vs. (6) Washington Commanders ... the Eagles top-ranked defense will be motivated to have a better all-around performance after giving up ...

  9. Cognitive hierarchy theory - Wikipedia

    en.wikipedia.org/wiki/Cognitive_Hierarchy_Theory

    Cognitive hierarchy theory (CHT) is a behavioral model originating in behavioral economics and game theory that attempts to describe human thought processes in strategic games. CHT aims to improve upon the accuracy of predictions made by standard analytic methods (including backwards induction and iterated elimination of dominated strategies ...