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Conditional probability: The probability that an event occurs given the outcome of other event. Generally written, Pr(A|B). For instance, the probability of a person being color blind provided that the person is male is about 0.1, and the corresponding probability given that the person is female is roughly 0.0001. It is not, certainly necessary that Pr(A|B)=Pr(B| A); the probability of having the spots given that the patient has measles, for instance, is very high, the probability of measles given that the patient has spots is, however, much less. If Pr(A|B)=Pr(A) then the events A and B are said to be self-governing.
A unified approach to all problems of prediction, estimation, and hypothesis testing. It is based on concept of the decision function, which tells the performer of experiment how t
Johnson''s Job Sequencing for n jobs and 2 machines
Nested design is the design in which levels of one or more factors are subsampled within one or more other factors such that, for instance, each level of a factor B happens at onl
Observation-driven model is a term generally applied to models for the longitudinal data or time series which introduce within the unit correlation by specifying the conditional
Quittingill effect is a problem which occurs most frequently in studies of the smoker cessation where smokers frequently quit smoking following the onset of the disease symptoms
regression line drawn as Y=C+1075x, when x was 2, and y was 239, given that y intercept was 11. calculate the residual
Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application inc
Residual plots are the plots of some type of residual which might be helpful in assessing the assumption made by the fitted model. In regression analysis there are various method
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 '
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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