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"Nuisance variable" has been used in the context of statistical surveys to refer information that is not of direct interest but which needs to be taken into account in an analysis. [ 4 ] In the context of stochastic models, the treatment of nuisance variables does not necessarily involve working with the full joint distribution of all the ...
Nuisance parameters are often scale parameters, but not always; for example in errors-in-variables models, the unknown true location of each observation is a nuisance parameter. A parameter may also cease to be a "nuisance" if it becomes the object of study, is estimated from data, or known.
Nuisance variable effect on response variable Nuisance variable (sex) effect on response variable (weight loss) In the examples listed above, a nuisance variable is a variable that is not the primary focus of the study but can affect the outcomes of the experiment. [3]
For example, the categorical variable(s) might describe treatment and the continuous variable(s) might be covariates (CV)'s, typically nuisance variables; or vice versa. Mathematically, ANCOVA decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance.
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 dependent variables are the intercepts and the slopes for the independent variables at Level 1 in the groups of Level 2. ... or "nuisance", variables. [4] ...
Variable annuities are not a one-size-fits-all solution to retirement planning. They may be suitable for some investors seeking a chance for higher growth and a guaranteed income stream, but for ...
Although the sampled values represent the joint distribution over all variables, the nuisance variables can simply be ignored when computing expected values or modes; this is equivalent to marginalizing over the nuisance variables. When a value for multiple variables is desired, the expected value is simply computed over each variable separately.