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Analysis is based on instrumental variable analysis of survey data on 430 rural households in 1996.
To data analysis, the statistical model of multi variable analysis of variance and Tukey test were applied.
Individual variable analysis of 23 parameters and the model development process included the determination of odds ratios and statistical tests for the predictive power and goodness-of-fit.
For each outcome variable, analysis of covariance will be performed on the data, conditioning on the baseline value.
Upon variable analysis of predictors of patient outcome (i.e. reaching the endpoint), both liquid score (deviance based χ statistic, pD = 5.75 × 10-4) and the diameter of the necrosis (pD = 6.68 × 10-4) were significantly associated with outcome.
To determine the effect of the nutrition intervention group versus the control group (the independent categorical variable) on the primary outcome of % weight loss (the dependent continuous variable), analysis of variance (ANOVA) will be used.
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Considering these two responses variables, Analysis of Variance has been performed in order to identify the effect of each input process parameters on the MRR and surface roughness.
For all variables, analysis of variance (ANOVA) for repeated measures was performed to assess the existence of significant within-group and between-group treatment effects.
Data analysis is done from following angles: analysis of the structure and utility of test samples, variables analysis of SMCCM and utility, model analysis of interaction between SMCCM and utility.
This measure of residual variance provides the optimum number of clustered topographies to explain the variance in topographical distribution, much like a latent-variable analysis of behavioural data would.
FCA [57] [59] is a kind of canonical analysis particularly well suited to describe associations between two qualitative variables (analysis of a contingency table crossing the terms of two variables).
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