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We addressed this issue by using weighted analysis and complex sample analysis in SPSS to adjust for sample weight, and multi stage sampling.
SHLPME parameters including nature of extracting solvent, headspace volume and design, extraction time, sample weight and microdrop volume were optimized.
A fractional factorial experimental design was developed to analyse the influence on the extraction process of seven different extraction variables: solvent, stirring, extraction temperature, extraction time, microwave power, sample weight and extraction volume.
Where SSW, SW, and OSW are the swollen sample weight, sample weight, and oil-absorbed sample weight, respectively [52].
The main parameters affecting the S3DE procedure, such as the water steam bubbling rate, extraction solvent volume, sample weight and S3DE time, were optimized.
Perusal of the data from Table 3 indicates that there is no significant change in power losses under different sample weight and different radiation conditions.
Similar(31)
Estimations using sample weights and 80 replicates for standard errors.
Analysis was by means of logistic and ordinary least-squares regression, with sample weights and design effects taken into account.
Linear regression, in which sample weights and design effects were taken into account, was used for the final models.
The reported sample sizes differ from the original (n = 44,634) due to sample weighting and lagging.
I apply NLSY79 sample weights and cluster standard errors around the lagged state.
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