# logistic curve

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## logistic curve

[lə′jis·tik ′kərv]
(statistics)
A type of growth curve, representing the size of a population y as a function of time t : y = k/ (1 + e -kbt ), where k and b are positive constants. Also known as Pearl-Reed curve.
More generally, a curve representing a function of the form y = k/ (1 + e cf (t)), where c is a constant and ƒ(t) is some function of time.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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Caption: Figure 1: Shape changes in the logistic curves under various parameter settings.
Modis and Debecker (1992) showed the graph (Figure 5) of bituminous coal production in the United States, with successive logistic curves fitted, representing the opening of a new market niche for the mineral just before mid-century.
The logistic curve fitted to samples taken during the summer of 2011, the year in which the winter survey occurred, was intermediate between the curves from the winter where fish that would skip spawning were classified as mature and the curve where fish that would skip spawning were classified as immature, and the fit was dissimilar to both, especially at moderate ages (Fig.
Although some numerical values of the health index are different, for example, the health index value of number 3 transformer is 0.007 and its value given by fuzzy-logic method is 0.3, they are not contradictory since the proposed method for health index calculation is based on the logistic curve. It is a common outcome on the transformers' health index, whose health conditions are good or very good because the initial stage of logistic curve is tending to zero.
Using statistical methods to choose the logistic curve that best fits the oil data yields 2014 as the year of peak oil production.
The dynamics of staminate flowering captured in the logistic curves for BegShed, MaxShed, and EndShed were used to create a Population Index ([P.sub.ind]) of the percentage of plants shedding pollen each day.
A 'Generalization' of the logistic curves and long-range forecasts (1966-1991) of residence telephones.
Table 1 shows parameter values of logistic curves fitted to derived h prior distributions as natural mortality (M) ranged from 0.05 to 0.7 and recruitment variability ([sigma]) ranged from 0.2 to 1.6.
Using an estimated split model (Millar and Walsh, 1992), we fitted logistic curves to these data by maximum likelihood method (Pope et al., 1975).
The parameter n is close to 1 corresponding to the logistic curve. Model (1) estimates the saturation level as 1785, near to logistic estimate (1689).

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