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  2. Fractional factorial design - Wikipedia

    en.wikipedia.org/wiki/Fractional_factorial_design

    The results of that example may be used to simulate a fractional factorial experiment using a half-fraction of the original 2 4 = 16 run design. The table shows the 2 4-1 = 8 run half-fraction experiment design and the resulting filtration rate, extracted from the table for the full 16 run factorial experiment.

  3. Aliasing (factorial experiments) - Wikipedia

    en.wikipedia.org/wiki/Aliasing_(factorial...

    In a fractional factorial experiment, the contrast vectors belonging to a given effect are restricted to the treatment combinations in the fraction. Thus, in the half-fraction {11, 12, 13} in the 2 × 3 example, the three effects may be represented by the column vectors in the following table:

  4. Orthogonal array - Wikipedia

    en.wikipedia.org/wiki/Orthogonal_Array

    An orthogonal array can be used to design a fractional factorial experiment. The columns represent the various factors and the entries are the levels at which the factors are observed. An experimental run is a row of the orthogonal array, that is, a specific combination of factor levels.

  5. Yates analysis - Wikipedia

    en.wikipedia.org/wiki/Yates_Analysis

    A fractional factorial design contains a carefully chosen subset of these combinations. The criterion for choosing the subsets is discussed in detail in the fractional factorial designs article. Formalized by Frank Yates , a Yates analysis exploits the special structure of these designs to generate least squares estimates for factor effects for ...

  6. Factorial experiment - Wikipedia

    en.wikipedia.org/wiki/Factorial_experiment

    Factorial experiments are described by two things: the number of factors, and the number of levels of each factor. For example, a 2×3 factorial experiment has two factors, the first at 2 levels and the second at 3 levels. Such an experiment has 2×3=6 treatment combinations or cells.

  7. Response surface methodology - Wikipedia

    en.wikipedia.org/wiki/Response_surface_methodology

    Designed experiments with full factorial design (left), response surface with second-degree polynomial (right) In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical ...

  8. Trump won't kill green energy - AOL

    www.aol.com/finance/trump-wont-kill-green-energy...

    Trump will probably make a show of eviscerating Biden’s climate plans while rebranding some of them as his own. Markets, in the end, may move in more or less the same direction.

  9. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    Design of experiments with full factorial design (left), response surface with second-degree polynomial (right) The design of experiments , also known as experiment design or experimental design , is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation.