However for moderate to large samples and from a programming point of view, this becomes a rather complex computational problem such that we would recommend either bootstrap or jackknife methodologies for the purpose of variance estimation.
For illustrative purposes, analysis of variance was used to compare TL and TA in individuals belonging to low, intermediate, and high cynical hostility score tertiles, adjusting for covariates.
For this purpose, analysis of variance was used.
The purpose of the variance component analysis was to determine if SNP in an annotation classes explain more of the variance than the same number of randomly chosen intergenic SNP.
While the GARCH model was applied for the purpose of capturing variance dynamics, the APARCH model was applied to test for the presence of asymmetric volatility effects in the variance dynamics of exchange rate returns.
The purpose of a variance components analysis of a calibration experiment is to determine, as precisely as the data allow, the calibration curve, i.e., the function that relates the measurement (logArea) to the stimulus (logConcentration), after eliminating the contributions of other factors and/or covariates to the data values.
For this purpose a homogeneity of variance analysis was performed with a repeated measures ANOVA, using the GENOVA computer program[ 29].
The tests that are used for this purpose are standard analysis of variance (ANOVA) and ANOVA adjusted for heteroscedasticity (Welch 1951).
Based on their poor item-scale correlation, items 40 and 50 were removed from all the analyses, including those presented in Table 1 and 2. For identification purposes, the variance of the eight factors was set to 1.00.
For this purpose, we conducted an analysis of variance (ANOVA) with the within-subjects factor time (screening and week 9) and the between-subjects factor group (intervention vs. control group).
