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Using ordinary point kriging, soil movement rates were interpolated at 7350 points in the sample grid.
Suppose that the sample has a sufficiently large size, and denote the points in the sample by.
A Kα beam diameter of 500 μm was used to analyze several points in the sample for comprehensive mineral identification.
This was done by bootstrap resampling the original inter-observation time distributions so that bootstrap sample sizes ranged from 100 up to the total number of data points in the sample.
It turns out that this approximation shows remarkable similarities to the AIC: \[ \text{BIC}[M] \; = \; - 2 \log P(s \mid h_{\hat{\theta}(s)}) + d \log n. \] Here $\hat{\theta}(s)$ is again the maximum likelihood estimate of the model, $d = dim(M $ the number of independent parameters, and $n$ is the number of data points in the sample.
The relative expression level was calculated using the relative expression software tool (REST©), which compares two treatment groups with multiple data points in the sample compared to the control groups and calculates the relative expression ratio between them.
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Though generally rapid and in the order of rates reported elsewhere (t½ < 5 h), some points in the sampling area showed greatly reduced turnover rates (t½ > 10 h).
A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow.
In theory, if a practice would move from the lowest group culture score to the highest (a difference of 55.6 points in this sample), the score on the quality indicator would decrease by 5.6 * 0.4 = 2.24 points.
When the participant touched any point in the sample, it disappeared.
Therefore, only the light reflected from the focal point in the sample goes through the detection pinhole and is then received.
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CEO of Professional Science Editing for Scientists @ prosciediting.com