Editing Logistic function (section)

=== Inflection Point Determination in Logistic Growth Regression ===
Logistic growth regressions carry significant uncertainty when data is available only up to around the inflection point of the growth process. Under these conditions, estimating the height at which the inflection point will occur may have uncertainties comparable to the carrying capacity (K) of the system.

A method to mitigate this uncertainty involves using the carrying capacity from a surrogate logistic growth process as a reference point.<ref>{{cite journal |last1=Vieira |first1=B.H. |last2=Hiar |first2=N.H. |last3=Cardoso |first3=G.C. |title=Uncertainty Reduction in Logistic Growth Regression Using Surrogate Systems Carrying Capacities: a COVID-19 Case Study |journal=Brazilian Journal of Physics |volume=52 |pages=15 |year=2022 |issue=1 |doi=10.1007/s13538-021-01010-6 |doi-access=free |bibcode=2022BrJPh..52...15V |pmc=8631260 }}</ref> By incorporating this constraint, even if K is only an estimate within a factor of two, the regression is stabilized, which improves accuracy and reduces uncertainty in the prediction parameters. This approach can be applied in fields such as economics and biology, where analogous surrogate systems or populations are available to inform the analysis.