interval estimation


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interval estimation

[′in·tər·vəl ‚es·tə‚mā·shən]
(statistics)
A technique that expresses uncertainty about an estimate by defining an interval, or range of values, and indicates the certain degree of confidence with which the population parameter will fall within the interval.
References in periodicals archive ?
Discussing probability and statistics needed by physicists and engineers, they cover random phenomena, probability, random variables, expected values, commonly used discrete distributions, commonly used density functions, joint distributions, some multivariate distributions, a collection of random variables, sampling distributions, estimation, interval estimation, tests of statistical hypotheses, model building and regression, and designing experiments and analyzing variance.
The interval estimation of [mu] with a significance level of [alpha] is as follows:
The more reliable method is the interval estimation. Thus 95% confidence Interval (CI) for mean difference in EDV8 and EDV16 is (-8.15220, 3.75220).
The most common techniques which are generalized for fuzzy data are presented as: Wu (2009) states that based on fuzzy measurements, confidence interval estimation for the fuzzy data is presented.
The Approximate Interval Estimation Model of Seismic Probability Risk.
This experiment has a significant impact on stage-specific control strategies and postmortem interval estimation.
Wang, "The interval estimation of reliability for probabilistic and non-probabilistic hybrid structural system," Engineering Failure Analysis, vol.
The data shown in Table 12 are obtained using interval estimation.
Dey [6] applied this by providing point and interval estimation methods for the scale parameter of the Rayleigh distribution under progressive Type-II censoring with binomial removal.
Panel A is based on interval estimation of Equation (1) and panel B is based on Probit estimation of Equation (2).
However the more general problem of interval estimation for a linear function of binomial proportions mentioned by Price and Bonett (2004), including pairwise comparisons, complex contrasts, interaction effects and simple main effects (BONETT; WOODWARD, 1987), are factors that influence the probability coverage estimate.