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For each module in the reference data (e.g. brain data) one observed a value Zsummary in the test data (e.g. a blood data set).
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For each summary statistic, the probability of observing a value lower than the observed value given the model was estimated.
In this case, we observe a value of 0.34.
Then, it follows that the probability of observing a value of zero is: P r y i = 0 x i β i = F - x i β i ⋅ (2).
Concerning the average measure of similarity (r) between the students' performances and the teachers' models, we observe a value of 0.69 (SD = 0.18).
Whenever any sensor observes a value greater than this threshold, the time of the alarm can be used as the originating time of the release.
The probability (P r ) of observing a value of one is: P r y i = 1 x i β i = 1 - F - x i β i ⋅ (1).
In our case, this theoretical proportion is 99.7% and we observe a value of 94.2% (Additional file 7).
The CDF for a given value of the independent variable (for example SI = x) describes the probability of observing a value less than or equal to x.
Assuming that the null hypothesis is correct, compute the probability (P -value) of observing a value for the test statistic that is as extreme or more extreme as the value that was actually observed.
Comparing T0,1 to this distribution enables the computation of adjusted p-value p1, i.e. the probability of observing a value as extreme as T0,1 for the most promising pair of SNPs.
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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