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Given data on competitive spending from a sample of customers, this study considers the problem of predicting consumer expenditures at competing firms when data is unavailable.
Designing a public transport system that incentivises low-carbon options such as cycling requires an understanding of its users – but when data is unavailable, the default is likely to benefit one particular demographic, usually the "average" man.
When data is unavailable or hard to obtain which is often the case for data on targets—estimations of financial performance can be assessed through surveys (Capron 1999).
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When data were unavailable or not stated explicitly, the reviewers coded the information as "not reported".
However, for 9 of 12 years for which we have rainfall data (1997 2009 excluding 2008 when data were unavailable), mean annual rainfall was lower than the long-term mean (Fig. S1).
However, when data were unavailable in an EMR, we retrieved data from the paper chart.
When data were unavailable by either of these approaches, we used other surveillance data, such as outbreak data.
Surveillance systems did not contribute to β s when data were unavailable (Additional file 1: Table S2).
That is, when data were unavailable, indicator scores were on average more often estimated as compared to calculated (Est = 91%; Cal = 9%).
We use the classical model of structured expert judgement, a method that has been used to quantify uncertainty in the areas of engineering and environmental risk assessment when data are unavailable.
This does not contradict a general nature of our framework; it is possible to add new moves as new types of data emerge, leading to improvement when using specific datasets, while not affecting the performance when such data is unavailable.
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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