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Treatment allocation ratio is the ratio of the number of subjects allocated to the two treatments in a clinical trial. The equal allocation is most usual in practice, but it might be advisable to allocate patients randomly in the other ratios when comparing a new treatment with the old one, or when one treatment is quite difficult or expensive to administer. The chance of detecting the real difference between the two treatments is not reduced much as long as the m ratio is not more extreme than 2:1.
Time series : The values of a variable recorded, generally at a regular interval, over the long period of time. The observed movement and fluctuations of several such series are
Weighted least squares is the method of estimation in which the estimates arise from minimizing the weighted sum of squares of the differences between response variable and its pr
L'Abbe ´ plot is often used in the meta-analysis of the clinical trials where the result is the binary response of it. The event risk (number of events/number of the patients in a
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
Attitude scaling : The process of analysing the positions of the individuals on scales purporting to measure attitudes, for instance a liberal-conservative scale, ora risk-willingn
How do I report the results in the table?
Bayesian confidence interval : An interval of the posterior distribution which is so that the density of it at any point inside the interval is greater than that of the density at
Glim is the software package specifically suited for fitting the generalized linear models (the acronym stands for the Generalized Linear Interactive Modelling), including the log
Lagging indicators: The part of a collection of the economic time series designed to give information about the broad swings in measures of the aggregate economic activity known a
Discuss the use of dummy variables in both multiple linear regression and non-linear regression. Give examples if possible
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