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When one updates data in and MDB the hypertable also changes the data values for all rows and for each time interval.
For each coefficient, data values for all image categories were normalized to the interval [0,1].
We therefore compared the results from sets of exposed and non-exposed patients matched using two different techniques: the first requiring full data values for all the match criteria and the second allowing missing data values for systolic blood pressure and HbA1c, where participants were then matched with a control with the same missing data value.
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Unless stated otherwise, all data values for forces and moments are presented as means+/−uncertainty at 95% confidence level.
The mean, often called the average, is computed by adding all the data values for a variable and dividing the sum by the number of data values.
As a result, only a small portion of data has values for all variables, while other large parts of data only have values for those routinely recorded variables.
According to this data, the values for all the variables were computed.
To give the readers the opportunity to reanalyze this data using less stringent approaches we present all count data with the P values for all four tests for the nine pairwise comparisons (Additional file 2).
Given the null hypothesis that the tested model is true, one can then generate data that is assumed to come from the true DGP, and for each data series calculate corresponding values for all these k test statistics.
This process was repeated 10 times, each time leaving out a random subset of the data, which enabled the generation of predicted values for all data points.
Table 1 was constructed using Microsoft Excel® and show mean data ± standard deviation values for all experiments.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com