enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    In the formula above we consider n observations of one dependent variable and p independent variables. Thus, Y i is the i th observation of the dependent variable, X ij is i th observation of the j th independent variable, j = 1, 2, ..., p. The values β j represent parameters to be estimated, and ε i is the i th independent identically ...

  3. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    Manipulation checks allow investigators to isolate the chief variables to strengthen support that these variables are operating as planned. One of the most important requirements of experimental research designs is the necessity of eliminating the effects of spurious, intervening, and antecedent variables. In the most basic model, cause (X ...

  4. Dependent and independent variables - Wikipedia

    en.wikipedia.org/wiki/Dependent_and_independent...

    A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...

  5. Single-subject design - Wikipedia

    en.wikipedia.org/wiki/Single-subject_design

    In order to determine the effect of the independent variable on the dependent variable, the researcher will graph the data collected and visually inspect the differences between phases. If there is a clear distinction between baseline and intervention, and then the data returns to the same trends/level during reversal, a functional relation ...

  6. Path analysis (statistics) - Wikipedia

    en.wikipedia.org/wiki/Path_analysis_(statistics)

    In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses (MANOVA, ANOVA, ANCOVA).

  7. Blinder–Oaxaca decomposition - Wikipedia

    en.wikipedia.org/wiki/Blinder–Oaxaca_decomposition

    Using Blinder-Oaxaca decomposition one can distinguish between "change of mean" contribution (purple) and "change of effect" contribution. The Oaxaca-Blinder decomposition (/ ˈ b l aɪ n d ər w ɑː ˈ h ɑː k ɑː /), also known as Kitagawa decomposition, is a statistical method that explains the difference in the means of a dependent variable between two groups by decomposing the gap into ...

  8. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    When there is a single level 1 independent variable, the level 1 model is = + +. refers to the score on the dependent variable for an individual observation at Level 1 (subscript i refers to individual case, subscript j refers to the group).

  9. Nonlinear regression - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_regression

    The independent or explanatory variable (say X) can be split up into classes or segments and linear regression can be performed per segment. Segmented regression with confidence analysis may yield the result that the dependent or response variable (say Y) behaves differently in the various segments. [3]