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This chapter uses a set of examples to demonstrate the use of mixed models analysis to address the analysis of data.
Candidate variables (P < 0.05) were entered into the multiple logistic regression models analysis to analyze risk factors affecting quit smoking.
To test whether results obtained were robust, we also used mixed models analysis to impute missing data.
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This paper presents further experimental study and model analysis to predict the occurrence of the biphasic growth pattern.
Past studies have been carried out by researchers with the help of model analysis to get more accurate information regarding wind structure interaction.
Ciais et al. [19] used model analysis to report that GPP was more important than total ecosystem respiration (TER) in determining African net biome productivity.
Odds ratios (OR) and their 95% confidence intervals (CI) were computed by logistic regression model analysis to clarify the impact of several potentially independent prognostic factors.
We used a paternal half-sib design and animal model analysis to estimate heritability and causal components of variance in vertebral number in three-spined sticklebacks (Gasterosteus aculeatus).
Surrogate variable analysis (SVA)[13], combines singular value decomposition (SVD) and a linear model analysis to estimate the eigenvalues from a residual expression matrix from which biological variation has already been removed.
We use stationary model analysis to investigate the reason for this diversity.
A mixed linear model analysis (to handle paired observations) adjusting for relevant covariates was performed.
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