Sentence examples for binary variable that does from inspiring English sources

To avoid bias from an unobserved binary variable that does not interact with treatment in its effect on outcome (and hence increase generalizability of results), one should use DIF or RR, but not OR, as an outcome measure.

Using the above framework, we address the following question, "What is the effect of intervention in a population in which a different fraction have an unobserved binary variable that does not interact with treatment in its effect on outcome?" We investigate this question for three common outcome measures, absolute difference (DIF), relative risk (RR), and odds ratio (OR).

Our response variable was a binary variable that reflected whether influenza was detected or undetected at a site.

In the case of binary classification problems, we assume that the output y is a discrete binary variable that has possible outcomes of 'recurrence' and 'non-recurrence'.

where Vacc i is any of the immunization variables listed in the summary statistics above for child i; NoBirthCert i is a binary variable that indicates if child i does not have a birth certificate; X i is a list of controls; γj are household dummies in some regressions and province or municipality dummies in others; and εi is the error of the equation.

The second measure is a binary variable that indicates a preference for nonconforming or unstructured activities or for doing things differently.

Each vertex is a binary variable that identifies that person as either carrying or not carrying the scabies mite.

Suppose now that the reported crime, denoted by the binary variable yb,i, does not coincide with y b, i ∗ since there is a probability of underreporting (in this context is referred to as misclassification of a true one as a zero)4.

gRecently, Erreygers [ 21] has shown that the standard concentration index, when applied to bounded indicators (such as binary variables) does not satisfy the mirror condition and suggested a correction.

Our preliminary tests show that the LP relaxation of the problem obtained by relaxing the integrality constraints on binary variables does not provide the same optimal solution with the original MIP model and the gap is actually significant in many cases.

We want to start a new conversation: one that doesn't establish unhealthy and unnecessary binaries.