This section combines the actual situation and development trend of each influencing factor, and uses

Logistic curve to estimate it.

The

logistic curve of cumulative relative frequency as a function of size class for females and males of M.

The selectivity described by the parameters [p.sub.il], [p.sub.i2], ...[p.sub.iL] for all carapace width classes was summarized by parametric functionf(Wc;[theta]) using the inverse

logistic curve which is the most commonly used among other sigmoid shapes (Holst, 2007) Equation 1:

where [alpha] (asymptote), [beta] (inflection point), and [lambda] (scaling parameter determining curve shape) are the 3 parameters of the

logistic curve to be estimated by nonlinear regression.

(3) The cumulative probability distribution of failure of the electrical components in distribution networks summarized in [14] is consistent with

logistic curves. The generalized logistic distribution function was assumed as the logarithmic time to the failure of the electrical insulation equipment [15].

The

logistic curve models the sigmoid function, where growth is initially exponential (increases by a constant percentage depending on time), after certain time slows down (growth rate decreases) and finally, in maturity, stops.

The

logistic curve is a simple and universal, generally used and tolerably reliable tool for constructing distant economic forecasts (Davis 1941).

Fitting the

logistic curve to data is presented through judicious selection of K (2).

Three approaches to curve fitting will be discussed in this article, viz., the fitting of certain curves that can be reduced to straight lines by transformation of the Y or X variable, fitting a polynomial curve in X which is often a good approximation to a more complex curve, and the asymptotic curve and

logistic curve which require more complex procedures.

Gompertz Curve: The

logistic curve closely resembles the half normal curve whereas the Gompertz curve is not normal but a skewed one.

Logistic regression, used for the prediction of the probability of occurrence of an event by fitting data to a

logistic curve, has become one of the most used statistical procedures employed by statisticians and researchers for the analysis of binary and proportional response data, according to Hilbe (emeritus, U.

Logistic regression is the term used to describe the process of fitting a

logistic curve to a dataset.