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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 associations between the pairs of variables and higher order interactions among more than two variables.
The estimated expected frequencies under the particular models can be found from the iterative proportional fitting. Such type of models is, essentially, the equivalent for the frequency data, of the models for the continuous data used in the analysis of variance, except that interest usually now centres on parameters representing interactions rather than those for the main effects.
An approach to decrease the size of very large data sets in which the data are first 'binned' and then statistics such as the mean and variance/covariance are calculated on each bi
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
Hello-goodbye effect : The phenomenon initially described in psychotherapy research, but one which might arise whenever a subject is assessed on two occasions, with some interventi
MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
Multi co linearity is the term used in the regression analysis to indicate situations where the explanatory variables are related by a linear function, making the inference of the
The analysis of data which are the functions observed continuously, for instance, functions of time. Basically a collection of statistical techniques or methods for answering quest
Clinical trials : Medical experiments designed to assess which of two or more treatments is much more effective. It is based on one of the oldest philosophy of the scienti?c resear
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.
Law of likelihood : Within framework of the statistical model, a particular set of data supports one statistical hypothesis or assumption better than another if the likelihood of t
Kaiser's rule is the rule frequently used in the principal components analysis for selecting the suitable the number of components. When the components are derived from correlati
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