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Bayesian confidence interval: An interval of the posterior distribution which is so that the density of it at any point inside the interval is greater than that of the density at any point outside and that the area which comes under the curve for that interval is equal to the prespeci?ed probability level. For any probability level there is basically only one such interval, which is also often called as the highest posterior density area. Unlike the usual con?dence interval related with the frequentist inference, here the intervals state the range within which parameters lie with a certain probability.
Normality - Reasons for Screening Data Prior to analyzing multivariate normality, one should consider univariate normality Histogram, Normal Q-Qplot (values on x axis
The procedure in which the prior distribution is required in the application of Bayesian inference, it is determined from empirical evidence, namely same data for which the posteri
This term sometimes used to describe the extra factor in variance of the sample mean when n sample values are drawn without the replacement from the finite population of size N. Th
Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting
The probability distribution which is a linear function of the number of component probability distributions. This type of distributions is used to model the populations thought to
Generally the final stage of an exploratory factor analysis in which factors derived initially are transformed to build their interpretation simpler. Generally the target of the pr
Conditional logistic regression : The form of logistic regression designed to work with the clustered data, such as data including matched pairs of the subjects, in which subject-s
Growth curve analysis is t he general term for methods dealing with development of the individuals over time. A classic instance includes recordings made on a group of children, sa
Grade of membership model: This is the general distribution free method for the clustering of the multivariate data in which only categorical variables are included. The model ass
The procedure in which initially the sample of subjects is selected for generating the auxillary information only, and then the second sample is selected in which the variable of i
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