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Bootstrap: The data-based simulation method/technique for the statistical inference which can be used to study the variability of the estimated characteristics of the probability distribution of a set of observations and give con?dence intervals for the parameters in situations where these are difficult or impossible to derive in the usual manner. (The use of term bootstrap derives from the phrase 'to pull oneself up by the one's bootstraps'.) The general idea and approach of the procedure involves sampling with the replacement to produce random samples of size n from the original data, x1; x2; ... ; xn; each of these is called as a bootstrap sample and each gives an approximate idea of the parameter of interest. Repeating the process the large number of times provides the desired information on the variability of the estimator and the approximate 95% con?dence interval can, for instance, be derived from the 2.5% and 97.5% quantiles of the replicate values.
Bayesian inference : An approach to the inference based largely on Bayes' Theorem and comprising of the below stated principal steps: (1) Obtain the likelihood, f x q describing
Chi-squared distribution : It is the probability distribution, f (x), of the random variable de?ned as the sum of squares of the number (v) of independent standard normal variables
sales per day for a product are as follows: x= 10, 11, 12, 13 (p)= 0.2, 0.4, 0.3, 0.1 obtain mean and variance of daily sale. if the profit is described by the following equation p
#how to analyse data
Ordered alternative hypothesis is a hypothesis or assumption which speci?es an order for the set of parameters of interest as an alternative to the equality, rather than simply th
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Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
a. Explain the meaning of the word non-orthogonal. b. What condition(s) must exist for non-orthogonality to occur? Be specific.
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
It is an informal method of assessing the effect of the publication bias, generally in the context of the meta-analysis. The effect measures from each of the reported study are plo
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