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  2. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope. [5] Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes.

  3. Mixed logit - Wikipedia

    en.wikipedia.org/wiki/Mixed_logit

    Mixed logit is a fully general statistical model for examining discrete choices.It overcomes three important limitations of the standard logit model by allowing for random taste variation across choosers, unrestricted substitution patterns across choices, and correlation in unobserved factors over time. [1]

  4. Non-negative least squares - Wikipedia

    en.wikipedia.org/wiki/Non-negative_least_squares

    In mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed to become negative. That is, given a matrix A and a (column) vector of response variables y , the goal is to find [ 1 ]

  5. Ridge regression - Wikipedia

    en.wikipedia.org/wiki/Ridge_regression

    Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]

  6. Multilevel modeling for repeated measures - Wikipedia

    en.wikipedia.org/wiki/Multilevel_Modeling_for...

    Random Effects: Random effects are the variance components that arise from measuring the relationship of the predictors to Y for each subject separately. These variance components include: (1) differences in the intercepts of these equations at the level of the subject; (2) differences across subjects in the slopes of these equations; and (3 ...

  7. Non-random two-liquid model - Wikipedia

    en.wikipedia.org/wiki/Non-random_two-liquid_model

    The non-random two-liquid model [1] (abbreviated NRTL model) is an activity coefficient model introduced by Renon and Prausnitz in 1968 that correlates the activity coefficients of a compound with its mole fractions in the liquid phase concerned. It is frequently applied in the field of chemical engineering to calculate phase equilibria.

  8. UNIFAC - Wikipedia

    en.wikipedia.org/wiki/UNIFAC

    The UNIFAC model was first published in 1975 by Fredenslund, Jones and John Prausnitz, a group of chemical engineering researchers from the University of California. Subsequently they and other authors have published a wide range of UNIFAC papers, extending the capabilities of the model; this has been by the development of new or revision of ...

  9. UNIQUAC - Wikipedia

    en.wikipedia.org/wiki/UNIQUAC

    The UNIQUAC model can be considered a second generation activity coefficient because its expression for the excess Gibbs energy consists of an entropy term in addition to an enthalpy term. Earlier activity coefficient models such as the Wilson equation and the non-random two-liquid model (NRTL model) only consist of enthalpy terms.