The principal measure of efficacy is
called Area Under the Curve (AUC), a measure of how well biomarkers identify true cases of disease (sensitivity) while avoiding false positives (specificity).
This analysis generates a statistic
called the area under the curve (AUC), which is proportional to the overall ability of the scale across its range of cut - offs to correctly identify both cases and non-cases.
Not exact matches
Libnits Made way to figure out
area under a
curve by dividing into smaller and smaller sections until width of each disappears,
call it calculus.
The effect of band widenning is a reduction in net upward LW flux (this is
called the radiative forcing), which is proportional to a change in
area under the
curve (a graph of flux over the spectrum); the contribution from band widenning is equal to the amount by which the band widens (in units ν) multiplied by - Fνup (CO2).