homoscedastic


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homoscedastic

(ˌhəʊməʊskɪˈdæstɪk)
adj
1. (Statistics) (of several distributions) having equal variance
2. (Statistics) (of a bivariate or multivariate distribution) having one variable whose variance is the same for all values of the other or others
3. (Statistics) (of a random variable) having this property
[C20: from homo- + scedastic, from Greek skedasis a scattering, dispersal]
homoscedasticity n
References in periodicals archive ?
All biometric relationships were first checked to avoid violation of the linearity and homoscedastic assumptions of simple linear regression by interpreting residual plots and histograms of residuals constructed using residPlot from the Fisheries Stock Assesment (FSA) package (Ogle 2017).
2], thus suggesting a homoscedastic distribution as a function of the speed.
We carry out several residuals analyses to investigate the suitability of the regression models presented in this study: normality tests were done to verify if the variables follow normal distributions; Durbin-Watson test to verify the independence of the residuals; and Breusch-Pagan and Goldfeld-Quandt test to verify if the residuals are homoscedastic.
Thus, homoscedastic values were calculated for Pearson's Correlation Coefficient (PCC) using R Statistical Package (Gentleman R and Ihaka R, Aukland, New Zealand), and values were plotted across correlation figures.
The White test for the fixed- effects model is based on the null hypothesis that the variance is homoscedastic (constant variance).
The results given in table three clearly shows the presence of heteroscedasticity by rejecting the null hypothesis of homoscedastic standard errors.
Data were tested and multivariate normality was revealed, which implies that data were normally distributed, linear, and homoscedastic (Kline, 2012).
The data of temperature and pH were normal and homoscedastic (Kolmogorov-Smirnov and Bartlett's tests).
Assuming the normal homoscedastic (NH) SDT model, and the yes/no experimental paradigms (MacMillan & Creelman, 2005), three main methods have been proposed for calculating the variance of d' (see below for a detailed technical presentation): the exact method of Miller (1996), the approximate method of Gourevitch and Galanter (1967), and the maximum likelihood method of Dorfman and Alf (1968).
The Engel and Granger method is applicable only when the cointegrating equation as the Equation (1) is homoscedastic.