# homoscedastic

(redirected from Homoscedasticity)
Also found in: Dictionary, Medical, Financial, Wikipedia.

## homoscedastic

[‚hä·mō·skə¦das·tik]
(statistics)
Pertaining to two or more distributions whose variances are equal.
Pertaining to a variate in a bivariate distribution whose variance is the same for all values of the other variate.
References in periodicals archive ?
The homoscedasticity assumption, as verified by the Akaike criteria, was more likely for all variables, except for the following variables: [k.
Levene tests conducted for the PII score average per product revealed the heterogeneity of the variance, showing the violation of the homoscedasticity assumption.
Finally, because first-order autocorrelation and group homoscedasticity are rejected respectively by the Wooldridge and Wald tests, the preferred model is the fixed effects model estimated by Feasible Generalized Least Squares (FGLS) or simple Fixed Effects by FGLS as in the fourth column of table 2.
Statistical analyses: The number of attacks on individuals similar and dissimilar to the holdfast and the difference between the size of male and female were compared by the Mann-Whitney test, because data did not follow homoscedasticity.
The Normality Assumption Test, The Homoscedasticity Assumption Test, The Linearity Assumption Test of each of the Independent Variables with the Dependent Variable, The Durbin-Watson d Statistic Test for Detecting Serial Correlation and The Multicollinearity Test, in trying to understand the significant and the insignificant variables.
Before analyzing the data, the tests for normality and homoscedasticity were completed on the full sample (N = 336) and the sample subset (n = 168).
In order to satisfy the requirements of the one-way ANOVA test, logarithmic transformation of data was applied to achieve homoscedasticity of the variable when necessary.
I checked for the homogeneity of variances of ranked data using the Brown-Forsythe test (Brown and Forsythe 1974) before performing Kruskal-Wallis tests, following the recommendations of Ruxton and Beauchamp (2008), which revealed homoscedasticity of variances.
We tested our data for homoscedasticity by plotting the residuals against the predicted values.
Assumptions of regression were confirmed for all analysis via an examination of residuals for normality and homoscedasticity (Sokal and Rohlf 1995).
Data were subjected to analysis of variance for each individual location, and after confirmation of homoscedasticity by the Hartley test (RAMALHO et al.
Site: Follow: Share:
Open / Close