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Lorenz curve: Essentially the graphical representation of cumulative distribution of the variable, most often used for the income. If the risks of disease are not monotonically increasing as the exposure becomes heavier, the data have to be arranged again from the lowest to the highest risk before the calculation of cumulative percentages. Associated with such type of curve is the Gini index defined as twice the area between curve and diagonal line. This index is lies between zero and one, with the larger values indicating the larger variability while smaller ones signify larger uniformity. The further the Lorenz curve lies below line of equality, the more unequal is the distribution of, the figure for this is given below
Hazard regression is the procedure for modeling the hazard function which does not depend on the suppositions made in Cox's proportional hazards model, namely that the log-hazard
Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and
There are two periods. You observe that Jack consumes 100 apples in period t = 0, and 120 apples in period t = 1. That is, (c 0 ; c 1 ) = (100; 120) Suppose Jack has the util
Cluster randomization : The random allocation of the groups or clusters of the individuals in the formation of treatment groups.Eeven though not as statistically ef?cient as the in
Incidental parameter problem is a problem which sometimes occurs when the number of parameters increases in the tandem with the number of observations. For instance, models for pa
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Prevalence : The measure of the number of people in a population who have a certain disease at a given point in time. It c an be measured by two methods, as point prevalence and p
Classification and regression tree technique (CART): The alternative to the multiple regression and associated techniques or methods for determining subsets of the explanatory va
Omitted covariates is a term generally found in the connection with regression modelling, where the model has been incompletely specified by not including significant covariates.
How is the rejection region defined and how is that related to the z-score and the p value? When do you reject or fail to reject the null hypothesis? Why do you think statisticians
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