# response variable

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## response variable

n
1. (Mathematics) statistics a more modern term for dependent variable2
2. (Psychology) statistics a more modern term for dependent variable2
References in periodicals archive ?
CT is a nonparametric statistical method used for the determination of the relationship between a response variable and the other variables that may be related to the response variable.
An alternative in this situation is the partial correlation coefficient, which allows the study of the degree of association between two traits, given the existence of a third trait or of a set of traits with the potential to influence the main response variable (CRUZ et al.
In this study, the weld strength as response variable was maximized by using FFD with the optimal level of the process parameters which has been obtained with the combination of individual parameters.
Compared to the others, the statistical models allow optimizing process parameters in short computational times and more than one response variable simultaneously.
Linear regression assumes that the unknown relation between the factor and the response variable is a linear relation Y = a + bx, where b is the coefficient of x.
2012), we first computed the individual linear regression model between each log-transformed response variable (IDW and GSI) and mean individual body size for each combination of levels of both factors.
The items were combined to create the indirect response variable for "pay late" behavior.
However, since this value represents an individual fit, such result did not interfere with the estimation process, because the optimal fit identified the fit of the controllable factors and that minimizes the variability of the response variable, regardless of whether the model is significant or not.
Quantile Regression is a method for estimating the relationship between a response variable and a set of explanatory variables for the whole conditional probability distribution of the response variable.
For these models it is common to consider the response variable following a normal distribution (when it is the best choice to model the biological characteristic), since this approach is theoretically consolidated from the formulation, adjustment methods and diagnostic analysis (residual analysis and influence), as observed in Diggle, Heagerty, Liang, and Zeger (2002), Fitzmaurice, Laird, and Ware (2004), Nobre and Singer (2007), Nobre and Singer (2011) with availability of use in applications such as R and SAS as verified in Pinheiro and Bates (2000) and Littel, Stroup, Wolfinger, and Schabenberger (2006).
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