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Categorical variable: A variable which provides the appropriate label of observation after the allocation to one of the several possible categories, for instance, the respiratory status can be excellent, terrible, fair, poor, good, or blood group which can be AB, A, B, or O. Respiratory status is an example of an ordered categorical variable or ordinal variable whereas blood type is an example of the unordered categorical variable.
Martingale: In the gambling context the term at first referred to a system for recouping losses by doubling the stake after each loss has occured. The modern mathematical concept
Graphical deception : Statistical graphics which are not as honest as they should be. It is relatively simple. To mislead the unwary with the graphical material. For instance, c
Model is the description of the supposed structure of a set of observations which can range from a fairly imprecise verbal account to, more commonly, a formalized mathematical exp
The time series for RESI1, HI1 and COOK1 have appeared again with different outlier values even though the 17 outliers found early were removed.
Coincidences : Astonishing concurrence of the events, perceived as meaningfully related, with no apparent causal connection. Such type of events abounds in everyday life and is oft
Nuisance parameter : The parameter of the model in which there is no scienti?c interest but whose values are generally required (but in usual are unknown) to make inferences about
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if |t | > t = 1.96
Bayesian network : It is essentially an expert system in which the uncertainty is dealt with using the conditional probabilities and Bayes' Theorem. Formally such type of network c
The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti
Imprecise probabilities is a n approach used by soft techniques in which uncertainty is represented by the closed, convex sets of probability distributions and the probability of
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