covariance analysis

covariance analysis

[kō′ver·ē·əns ə‚nal·ə·səs]
(statistics)
An extension of the analysis of variance which combines linear regression with analysis of variance; used when members falling into classes have values of more than one variable.
References in periodicals archive ?
Soil mobilization times (before and after soil tillage) were evaluated based on covariance analysis using the SAS statistical software.
When the differences between the groups were reanalyzed after controlling HADS depression scores with covariance analysis, the significant difference in ASEX and WHOQOL-BREF-TR physical domain scores between the groups remained, but the significant difference in WHOQOL-BREF-TR psychological domain scores disappeared.
In order to analyze data the following features were measured: frequency, percentage, standard deviation, T, covariance analysis, and the Kolmogorov-Smirnov test.
The results showed that the test is not significant at 0/05 (Box's M=33/00, F= 1/22 F, SIG= 0/22) and therefore, it can be used from multivariate covariance analysis for testing this issue (MANCOVA).
Linear regression relationships between SL, LW, and Wt were calculated and compared through the use of covariance analysis to determine if there was a relationship between sex and shell size or shape (Stoner et al.
Statistical analysis was performed by SPSS (version 13) using independent t-test, Pearson's correlation coefficient and covariance analysis of covariance.
Covariance analysis was performed to see the association of liver function tests and MS (correcting for age, smoking, and gender).
The EM is determined using a time-varying scheme of the classical Maximum Covariance Analysis, in which the predictor or the predictand field is compiled including all the considered lags in the array.
Covariance analysis showed that serial autocorrelation was not a factor influencing recruitment within populations, and differences in precipitation accounted for 38.
After introducing basic statistical concepts and simple linear regression, he discusses multiple linear regression procedures and matrix algebra, aspects of correlation analysis and partial correlation analysis, common problems of multiple linear regression such as multiple collinearity and ridge regression bias, polynomial regression and its uses, residual analysis, the use of indicator or dummy variables, forward and stepwise selections of xi and backward elimination in statistical software, and covariance analysis.
The covariance analysis resulted in tillage variance remaining relatively unchanged while the variance associated with site was reduced 40-fold.