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  2. Fractional factorial design - Wikipedia

    en.wikipedia.org/wiki/Fractional_factorial_design

    An important characteristic of a fractional design is the defining relation, which gives the set of interaction columns equal in the design matrix to a column of plus signs, denoted by I. For the above example, since D = AB and E = AC, then ABD and ACE are both columns of plus signs, and consequently so is BDCE: D*D = AB*D = I. E*E = AC*E = I

  3. 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 ...

  4. Aliasing (factorial experiments) - Wikipedia

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

    A fractional factorial design is said to have resolution if every -factor effect [note 4] is unaliased with every effect having fewer than factors. For example, a design has resolution R = 3 {\displaystyle R=3} if main effects are unaliased with each other (taking p = 1 ) {\displaystyle p=1)} , though it allows main effects to be aliased with ...

  5. One-factor-at-a-time method - Wikipedia

    en.wikipedia.org/wiki/One-factor-at-a-time_method

    The latter is far from optimal, but the former, which changes only one variable at a time, is worse. See also the factorial experimental design methods pioneered by Sir Ronald A. Fisher. Reasons for disfavoring OFAT include: OFAT requires more runs for the same precision in effect estimation; OFAT cannot estimate interactions

  6. Central composite design - Wikipedia

    en.wikipedia.org/wiki/Central_composite_design

    The design matrix for a central composite design experiment involving k factors is derived from a matrix, d, containing the following three different parts corresponding to the three types of experimental runs: The matrix F obtained from the factorial experiment. The factor levels are scaled so that its entries are coded as +1 and −1.

  7. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [12] by Abraham Wald in the context of sequential tests of statistical hypotheses. [13]

  8. Khloé Kardashian Drank an 'Entire Bottle of Vodka,' Wrestled ...

    www.aol.com/khlo-kardashian-drank-entire-bottle...

    Khloé explained that they didn't know at the time of the rehearsal dinner that they would be flown to Italy for the actual ceremony, so they had "a drinking competition." "I took an entire bottle ...

  9. Box–Behnken design - Wikipedia

    en.wikipedia.org/wiki/Box–Behnken_design

    The design with 7 factors was found first while looking for a design having the desired property concerning estimation variance, and then similar designs were found for other numbers of factors. Each design can be thought of as a combination of a two-level (full or fractional) factorial design with an incomplete block design. In each block, a ...