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The type of longitudinal study in which the subjects receive different treatments on the various occasions. Random allocation is required to determine the order in which the treatments are received. The simplest such design includes two groups of the subjects, one of which receives each of the two treatments, A and B, in the order AB, while the other takes them in the reverse order. This is called as a two-by-two crossover design. While the treatment comparison is 'within-subject' rather than the 'between-subject', it is likely to need fewer subjects to achieve the given power. The study of such designs is not necessarily straight- forward because of the possibility of the carryover effects, that is left over effects of the treatment received on the first occasion which remain present into the second occasion. An attempt to minimize this problem is many times made by including the wash-out period between the two treatment occasions. Some of the authorities have suggested that this type of design should only be used if such carryover effects can be ruled out a priori. Crossover designs are only applicable to the chronic conditions for which short-term relief of the symptoms is the goal rather than a cure of it.
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Initial data analysis (IDA): The first phase in the examination of the data set which comprises number of informal steps including the following steps * checking the quality o
Laplace distribution : The probability distribution, f(x), given by the following formula Can be derived as the distribution of the difference of two independent random var
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This term is sometimes used for the data collected in those longitudinal studies in which more than the single response variable is recorded for each subject on each occasion. For
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The special cases of the probability distributions in which the random variable's distribution is concentrated at one point only. For instance, a discrete uniform distribution when
Kaiser's rule is the rule frequently used in the principal components analysis for selecting the suitable the number of components. When the components are derived from correlati
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Non linear mapping (NLM ) is a technique for obtaining a low-dimensional representation of the set of multivariate data, which operates by minimizing a function of the differences
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