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Multilevel modeling is a generalized form of linear regression used to analyze variance in experimental outcomes predicted by both individual (within-participants) and group (between-participants) variables.
Linear regression used
*Multiple Linear Regression used Enter Method.
Linear regression used the median values of the measurements made at each transfer step.
Linear regression used random effects to adjust for between-patient variations.
To determine the lowest concentration at which oxygen saturation states may be correctly measured, the correlation coefficient of the linear regression used to determine concentration in section 3.2 was calculated.
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Trends over time were evaluated with linear regression using year as the predictor variable.
We then conducted a generalized linear regression using XL-STAT 2013.5 statistical package.
Empirical equations to predict the MRR were developed through linear regression using experimental data.
The 85th percentile speed prediction models are developed by stepwise linear regression using the Statistical Package for Social Sciences (SPSS).
The data between zooplankton and physico-chemical characteristics were subjected to correlation and linear regression using IBM-SPSS (v20.0).
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