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  2. Mixed-design analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Mixed-design_analysis_of...

    [5] [page needed] The main difference between the sum of squares of the within-subject factors and between-subject factors is that within-subject factors have an interaction factor. More specifically, the total sum of squares in a regular one-way ANOVA would consist of two parts: variance due to treatment or condition (SS between-subjects ) and ...

  3. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    These are efficient at evaluating the effects and possible interactions of several factors (independent variables). Analysis of experiment design is built on the foundation of the analysis of variance, a collection of models that partition the observed variance into components, according to what factors the experiment must estimate or test.

  4. Analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_variance

    Linearly re-order the data so that -th observation is associated with a response and factors , where {,, …,} denotes the different factors and is the total number of factors. In one-way ANOVA B = 1 {\displaystyle B=1} and in two-way ANOVA B = 2 {\displaystyle B=2} .

  5. Factorial experiment - Wikipedia

    en.wikipedia.org/wiki/Factorial_experiment

    Similarly, a 2×2×3 experiment has three factors, two at 2 levels and one at 3, for a total of 12 treatment combinations. If every factor has s levels (a so-called fixed-level or symmetric design), the experiment is typically denoted by s k, where k is the number of factors. Thus a 2 5 experiment has 5 factors

  6. Interaction (statistics) - Wikipedia

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

    Interaction effect of education and ideology on concern about sea level rise. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).

  7. Fractional factorial design - Wikipedia

    en.wikipedia.org/wiki/Fractional_factorial_design

    Dropping B results in a full factorial 2 3 design for the factors A, C, and D. Performing the anova using factors A, C, and D, and the interaction terms A:C and A:D, gives the results shown in the table, which are very similar to the results for the full factorial experiment experiment, but have the advantage of requiring only a half-fraction 8 ...

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  9. Dependent and independent variables - Wikipedia

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

    [6] [7] It is possible to have multiple independent variables or multiple dependent variables. For instance, in multivariable calculus, one often encounters functions of the form z = f(x,y), where z is a dependent variable and x and y are independent variables. [8] Functions with multiple outputs are often referred to as vector-valued functions.