The phrase "a logistic distribution" is correct and usable in written English.
It can be used in contexts related to statistics, probability, or data analysis when discussing a specific type of probability distribution.
Example: "In our analysis, we found that the data followed a logistic distribution, which is useful for modeling binary outcomes."
Alternatives: "a logistic function" or "a logistic model".
It is customary to assume a normal or a logistic distribution.
Moreover, we prove that the 'local entropy function', related to a logistic distribution, is a catenary and vice versa.
Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation.
It is assumed that the data follows a logistic distribution (Damodar 2004).
While probit model assumes normal distribution error term, the logit model takes a logistic distribution of the error term.
Using the simulated data, we obtain simulated recurrence times of earthquakes, and fit a logistic distribution to these times.
At equilibrium, under the weak-mutation regime (Sella and Hirsh 2005; Shah and Gilchrist 2011b; McCandlish and Stoltzfus 2014), the expected frequency of observing a synonymous codon i (p i) of an amino acid in a gene with an average protein synthesis rate Φ follows a multinomial logistic distribution.
A random variable Y is said to have a discrete logistic distribution Chakraborty and Chakravarty (2013) with parameter p (0 < p < 1) and − ∞ < μ < ∞, if its pmf has the form Pleft(Y=kright)=frac{left 1-pright){p}^{k-mu }}{left(1+{p}^{k-mu}right)left(1+{p}^{k-mu +1}right)},;kin mathbf{Z}.
Using this discretization idea, Chakraborty and Chakravarty (2016) have proposed a discrete logistic distribution starting from the continuous two-parameter logistic distribution.
The dependent measure was the dichotomous outcome of hard or easy task choice, and we used a binary logistic distribution to model the probability of choosing the hard-task.
A standard result on logit models is that we can represent the outcomes y ij as thresholded versions of a latent continuous quantity z ij (Holmes et al. 2006): y i j = { 1 if z i j ≥ 0, 0 if z i j < 0. z i j = α + β i + ε i j , where ε ij follows a standard logistic distribution.
