# distribution

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## Synonyms for distribution

### the commercial activity of transporting and selling goods from a producer to a consumer

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From Theorem 1, the extended Student's t-distribution has density (10) and from Theorem 3 we can generate random variables X ~ Et(v, [xi]).
The standard Cauchy distribution (Student's t-distribution with one degree of freedom) has neither a moment-generating function nor finite moments of order greater than or equal to one [Johnson et al., 1994].
Under the null hypothesis, [T.sub.2] always follows t-distribution with df = n-1.
(18) The t-distribution (also known as the Student's t-distribution) is a probability distribution that is used to estimate population parameters when the sample size is small and/or when the population variance is unknown.
where, [theta] represents the given model parameter; [[omega].sub.ij] [[omega].sub.ij] represents the relevant weight between outermost layer node [v.sub.i] [v.sub.i] and hidden layer node [h.sub.j][h.sub.j]; [b.sub.i][b.sub.i] stands for the offset of outermost layer node; [[alpha].sub.i] [[alpha].sub.j] represents the offset of hidden layer node; u is the freedom degree of T-distribution function which is used to control the change of distribution form; u/(u-2)is the variance yields of T-distribution function.
Here we compare the results of the previous two sections using a specific non-Gaussian model, namely, Student's t-distribution as an example.
The draw back of the ([lambda], [bar.[alpha]], [mu], [SIGMA], [gamma])-parametrization is that it does not exist when [bar.[alpha]] = 0 and [lambda] [member of] [-1, 0], which corresponds to a Student's t-distribution without variance.
If the event is absent, y has Student's t-distribution with df = N - 1 degrees of freedom.
At 240 minutes, the probability of a result above 5.5 mM occurring was 0.14%, being 3.26 SD from the mean (2-tailed t-distribution).
For each data set consisting of 30 catch-effort coordinates, we calculated a 95% confidence interval (CI) for cpue based on t-distribution. The variances for each estimator were calculated as in Snedecor and Cochran (1967).
This assignment rehearses the sampling or x distribution, which is essential to understanding the t-distribution that is used in the final project.
Appendices include a review of elementary set theory, a standard normal distribution table, t-Distribution and answers to all problems.
With the choice of five degrees of freedom, the t-distribution exhibits extreme fat tails and potentially presents a serious challenge for our finite sample results that are derived under the normality assumption.
Since the effective degree of freedom is more than 30, from the t-distribution table at 95% CL, the coverage factor
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