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The number of teaching physicians had a significant and negative effect modification on salary, implying an antagonistic effect between these two attributes (ε= −1.5223, P = 0.038).
Understanding the relationship between these two attributes in such situations could have broad implications for clinical performance training, assessment, and research.
Association between age and sex was checked by applying chi square test and it was found that no association existed between these two attributes (χ2 = 0.098 and p = 0.99).
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It is claimed that the efficiency of the chosen methodology relies on the balance between these three attributes.
Clearly, through repeated choices, the indifference curve can be interpolated, with Δm/Δk denoting the MRS between the two attributes.
end{aligned}Take the distance between the two attributes Age and Children in Table 2 as an example.
The distance between the two attributes is thus calculated as: begin{aligned} d(mathrm{PR, DM} =frac{1}{mathrm{Avg}(0.6, 0.6)}=1.67.
Consider a multivariate binomial process with two correlated attributes, an in-control parameter vector [N', P 0 '] = [(20, 30)', (0.2, 0.15)'] and a correlation between the two attributes of 0.25.
Israeli Jews would have to make a choice between the two attributes if they continue ruling the land.
However, error rates differed significantly between the two attributes (color, position), F 1, 74) = 72.21, p<.01, with more errors in the position than in the color conditions.
The error rates differed significantly between the two attributes, F 1, 55) = 54.10, p<.01, with more errors in the position than in the color conditions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com