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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 and given as follows
The shape parameter, v, is generally known as the degrees of freedom of the distribution. This distribution occurs in number of areas of the statistics, for instance, assessing the goodness-of-?t of models, specifically those ?tted to contingency tables. The mean of distribution is v and its variance is 2v.
Conjugate prior : The distribution for samples from the particular probability distribution such that the posterior distribution at each stage of the sampling is of the identical f
Collapsing categories : A procedure generally applied to contingency tables in which the two or more row or column categories are combined, in number of cases so as to yield the re
A rule for computing the number of classes to use while constructing a histogram and can be given by here n is the sample size and ^ γ is the estimate of kurtosis.
Categorizing continuous variables : A practice which involves the conversion of the continuous variables into the series of the categories, which is common in the field of medical
Log-linear models is the models for count data in which the logarithm of expected value of a count variable is modelled as the linear function of parameters; the latter represent
Protocol is the formal document outlining the proposed process for carrying out the clinical trial. The basic features of the document are to study the objectives, patient selecti
Generalized poisson distribution: The probability distribution can be defined as follows: The distribution corresponds to the situation in which the values of the rand
Mosaic displays is the graphical display of the standardized residuals from the fitting a log-linear model to a contingency table in which the colour and outline of the mosaic's '
Please help with following problem: : Let’s consider the logistic regression model, which we will refer to as Model 1, given by log(pi / [1-pi]) = 0.25 + 0.32*X1 + 0.70*X2 + 0.
Locally weighted regression is the method of regression analysis in which the polynomials of degree one (linear) or two (quadratic) are used to approximate regression function in
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