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Both logistic and normal distributions are symmetric with a basic, unimodal "bell curve" shape.
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We used logistic and normal regression models for binary and continuous outcomes, respectively.
Logistic and normal regression models were used to evaluate the associations with SNPs in cases and controls, and outcomes in patients with infection associated ALI.
With a simple multiplicative transformation of the scale, the logistic and normal ogive curves are very similar and indistinguishable for all practical work (see, for instance, Lord 1980).
The models are parametric and are the cumulative probability distribution functions (cdfs) for the well-known Weibull, log normal (probit), log logistic, gamma (multi-hit), and linear exponential (single hit) distributions as well as the truncated logistic and truncated normal distributions.
The major alternatives are the logistic and the normal.
The proposed model is an alternative to the traditional extreme value (or log-Weibull), logistic and log-normal models, among others.
The best fitting distributions for both studies were the Log-Logistic and Log-Normal.
The most commonly used parametric time-to-event models are the Weibull, log-logistic and log-normal distributions.
However, for Weibull and Nakagami, it is a different case, which implies that log-logistic and log-normal are much more accurate to model foliage clutter.
The log-logistic and log-normal distributions belong to the accelerated failure time (AFT) family, and are useful in modeling nonmonotone hazard rates (Lawless 2002).
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