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Point scoring is an easy distribution free method which can be used for the prediction of a response which is a binary variable from the observations on several explanatory variables which are also binary in nature. The easiest version of the process, often known as the Burgess technique, operates by first taking the explanatory variables one at a time and then determining which level of each variable is related with the higher proportion of 'success' category of the binary response. The prediction score for any of the individual is then just the number of explanatory variables at the high level (generally only variables which are ' significant' are included in the score). The score thus varies from 0, when all explanatory variables are at low level, to its maximum value when all important variables are at the high level. The goal of the technique is to split the population into risk groups.
Missing values : The observations missing from the set of data for some of the reason. In longitudinal studies, for instance, they might occur because subjects drop out of the stud
Hazard function : The risk which an individual experiences an event in a small time interval, given that the individual has survived up to the starting of the interval. It is th
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
how does it work exactly
Greenhouse geissercorrection is the method of adjusting the degrees of freedom of the within- subject F-tests in the analysis of the variance of longitudinal data so as to allow t
Clinical vs. statistical significance : The distinction among results in terms of their possible clinical importance rather than simply in terms of their statistical importance. Wi
Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
Complier average causal effect (CACE): The treatment effect amid true compliers in the clinical trial. For the suitable response variable, the CACE is given by the difference in o
importance of mathamatical expection in business
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