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Clinical vs. statistical significance: The distinction among results in terms of their possible clinical importance rather than simply in terms of their statistical importance. With large samples, for instance, very small differences which have little or no clinical importance may turn out to be the statistically signi?cant products. The practical implications of any ?nding in the medical investigation should be judged on the clinical as well as the statistical grounds.
Multiple imputation : The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simula
Interval-censored observations are the observations which often occur in the context of studies of time elapsed to the particular event when subjects are not monitored regularl
Non linear model : A model which is non-linear in the parameters, for instance are Some such type of models can be converted into the linear models by linearization (the s
Barnard, George Alfred (1915^2002) : Born in Walthamstow in the east of London, Barnard achieved a scholarship to St. John's College, Cambridge, from where he graduated in the math
Reciprocal transformation is a transformation of the form y =1/x, which is specifically useful for certain types of variables. Resistances, for instance, become conductances, and
Designs which permits two or more questions to be addressed in the investigation. The easiest factorial design is one in which each of the two treatments or interventions are p
Prepare a 1,400- to 1,750-word paper in which you formulate a hypothesis based on your selected research issue, problem, or opportunity. Address the following: •Describe your sele
Procedures for estimating the probability distributions without supposing any particular functional form. Constructing the histogram is perhaps the easiest example of such type of
Thomas Economic Forecasting, Inc. and Harmon Econometrics have the same mean error in forecasting the stock market over the last ten years. However, the standard deviation for Thom
Over dispersion is the phenomenon which occurs when empirical variance in the data exceeds the nominal variance under some supposed model. Most often encountered when the modeling
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