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Probability distribution: For the discrete random variable, a mathematical formula which provides the probability of each value of variable. See, for instance, binomial distribution and Poisson distribution. For the continuous random variable, a curve described by the mathematical formula which specifies, by way of area under the curve, the probability which the variable falls within the particular interval. Examples comprise the normal distribution and the exponential distribution. In both the cases the term probability density might also be used. (A distinction is sometimes made among 'density' and 'distribution', when the latter is reserved for probability that the random variable comes below some value. In this dictionary, though, the latter will be termed the cumulative probability distribution and the probability distribution and probability density both are used synonymously.
Non linear model : A model which is non-linear in the parameters, for instance are Some such type of models can be converted into the linear models by linearization (the s
Relative risk is the measure of the association between the exposure to a particular factor and the risk or probability of a convinced outcome, calculated as follows therefor
The functions of the data and the parameters of interest which can be brought in use to conduct inference about the parameters when full distribution of the observations is unknown
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
Least significant difference test is an approach to comparing a set of means which controls the family wise error rate at some specific level, let's assume it to be α. The hypothe
The theorem relating structure of the likelihood to the concept of the sufficient statistic. Officially the necessary and sufficient condition which a statistic S be sufficient for
Hanging rootogram is he diagram comparing the observed rootogram with the ?tted curve, in which dissimilarities between the two are displayed in relation to the horizontal axis,
Principal components regression analysis is a process often taken in use to overcome the problem of multicollinearity in the regression, when simply deleting a number of the expla
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Bayes factor : A summary of evidence for the modelM1 against the another modelM0 provided by the set of data D, which can be used in the model selection. Given by the ratio of post
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