A generalized least squares (GLS) statistical mixed modelling approach was used [40], [41] to account for the unequal variance imposed by the experimental design using suitable variance-covariate functions.
We used the Akaike information criteria and likelihood ratio tests to examine the significance of serial correlation (repeated statement) as well as to model random effects, along with a suitable variance-covariance matrix structure.
We used Akaike information criteria and the likelihood ratio test to examine the significance of serial correlation (in repeated statement) as well as to model random effects, along with a suitable variance-covariance matrix structure.
The results were analyzed by using ANOVA, i.e., analysis of variance suitable for the experimental design.
In order to make data suitable for the variance analyses, age was used as a categorical variable in this analysis.
ANOVA is not suitable when the variance between male and female objects in one dimension is unequal.
The Poisson regression model is simpler than negative binomial regression and is suitable when the variance in the outcome variable within each treatment group is approximately equal to the mean (Faraway, 2006).
In contrast, when small target regions need to be examined, especially in many samples, PCR-based approaches may be preferred as they enable a deep and even coverage over the region of interest, suitable for genetic variance analysis.
Differences in all parameters of interest were assessed by an analysis of variance suitable for repeated measures with four repeated within factors (i.e. the PEEP levels) and either no between factor (main analysis) or one between factor (group) in the post hoc analysis.
Under suitable conditions, the variances of the relative errors in the scaled model are a quadratic function of forecast lead time with two parameters (since the error variance at lead time zero is zero).
The obtained data of chromium removal, and residual tannic acid was subjected to analysis of variance (ANOVA), suitable to the design of experiments.
