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. 2015 Mar 20;59:10.3402/fnr.v59.25932. doi: 10.3402/fnr.v59.25932

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© 2015 Mona Bjelland et al.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

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Fig. 1 — The Johnson-Neyman technique. When heterogeneous regression slopes are present this implies that the magnitude of the intervention effect (Y) is not the same at different levels of X (covariate; the baseline intake in these analyses). The Johnson-Neyman approach provides values on X associated with non-significant/significant effects, giving regions of non-significance and significance. X_L1 is the lowest value and X_L2 is the highest value of the non-significance region.