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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]
The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing ...
Sometimes called dependent variable(s). Response surface: A designed experiment that models the quantitative response, especially for the short-term goal of improving a process and the longer-term goal of finding optimum factor-values. Traditionally, response-surfaces have been modeled with quadratic-polynomials, whose estimation requires that ...
In the design of experiments, completely randomized designs are for studying the effects of one primary factor without the need to take other nuisance variables into account. This article describes completely randomized designs that have one primary factor.
The term "design effect" was coined by Leslie Kish in his 1965 book "Survey Sampling." [1]: 88, 258 In it, Kish proposed the general definition for the design effect, [a] as well as formulas for the design effect of cluster sampling (with intraclass correlation); [1]: 162 and the famous design effect formula for unequal probability sampling.
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 techniques to relate input variables, otherwise known as factors, to the response.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
It was shown that the Bayesian design is superior to D-optimal design. The Kelly criterion also describes such a utility function for a gambler seeking to maximize profit, which is used in gambling and information theory ; Kelly's situation is identical to the foregoing, with the side information, or "private wire" taking the place of the ...