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We expressed the effect of a risk factor as an odds ratio with 95% confidence interval where possible, but where the data did not allow this we compared the mean and standard deviation of the risk factor between those individuals with and without diabetic ketoacidosis and expressed this as mean and standard error.
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In every field but economics, where the data indicated no gender preference, the respondents strongly preferred the purportedly female candidates.
But that's where the data trail ended.
A decent model will do better than you at recognizing how demographic and other variables predict movie attendance, but the place where the data in the model stops is an opportunity for humans to add something.
That is not to say that ranking organisations or public bodies should not happen– but in contexts where the data can be treated with appropriate caution and care, and where those producing them are perceived to be acting in the best interests of the system.
It was a difficult choice for us to make, but we followed where the data pointed.
But where is the data going?
But where were the data?
But where do the data come from, and how are they processed?
For Phase One we used GNI per capita for 1992 provided by the World Bank World Bankk 2009), but where these data were missing (five countries) Phase One GNI was set equal to Phase Three GNI.
Data in this class is only stored in a single region, so it may not be quite as available as data stored in the multi-regional service (Google's SLA promises a 99.9percentt uptime), but for many workloads where the data only needs to be close to where the compute happens, using this cheaper storage service should be just fine.
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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