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In a multivariate model (ROC analysis), the area under the curve is 0.705 ,  explain how this is statistically significant.  Images of the predictiv

In a multivariate model (ROC analysis), the area under the curve is 0.705 ,  explain how this is statistically significant.  Images of the predictiv

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In a multivariate model (ROC analysis), the area under the curve is 0.705 ,  explain how this is statistically significant. 

Images of the predictive power graph is attached. Please help explain the results. 

100,0% 80,0% 60,0% 40,0% 20,0% 0,0% Serum Zonulin Predictive Power A 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,011,012,013,0 Sensitivity Specificity Sensitivity %0 100% 80%- 60%- 40%- 20% 0%+ 0% %20 %40 20% %60 AUC 0.705 (0.617-0.792). p=0.000 40% 1 - Specificity %80 60% %100 %100 %80 %60 %40 %20 +%0 80% 100% B Figure 3. A-The diagrammatic illustration of serum zonulin predictive power in case of sensitivity and specificity. B-The efficiency of plasma zonulin levels is found statistically significant [area under the curve 0.705 (0.617-0.792)].
Transcribed Image Text:100,0% 80,0% 60,0% 40,0% 20,0% 0,0% Serum Zonulin Predictive Power A 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,011,012,013,0 Sensitivity Specificity Sensitivity %0 100% 80%- 60%- 40%- 20% 0%+ 0% %20 %40 20% %60 AUC 0.705 (0.617-0.792). p=0.000 40% 1 - Specificity %80 60% %100 %100 %80 %60 %40 %20 +%0 80% 100% B Figure 3. A-The diagrammatic illustration of serum zonulin predictive power in case of sensitivity and specificity. B-The efficiency of plasma zonulin levels is found statistically significant [area under the curve 0.705 (0.617-0.792)].
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