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Longitudinal data: The data arising when each of the number of subjects or patients give rise to the vector of measurements representing same variable observed at the number of different time instants.
This type of data combines elements of the multivariate data and time series data. They differ from the previous, however, in that only a single variable is involved, and from the latter in consisting of a large number of short series, one from the each subject, rather than single long series. This kind of data can be collected either prospectively, following subjects forward in time, or the retrospectively, by extracting measurements on each person from historical records. This kind of data is also often called as repeated measures data, specifically in the social and behavioural sciences, though in these disciplines such data are more likely to occur from observing individuals repeatedly under different experimental conditions rather than from a simple time sequence. Special statistical techniques are often required for the analysis of this type of data because the set of measurements on one subject tend to be intercorrelated. This correlation should be taken into account to draw the valid scientific inferences. The design of most of the studies specifies that all the subjects are to have the same number of the repeated measurements made at the equivalent time intervals. Such data is usually referred to as the balanced longitudinal data. But though the balanced data is generally the target, unbalanced longitudinal data in which subjects might have different numbers of repeated measurements made at the differing time intervals, do arise for the variety of reasons. Sometimes the data are unbalanced or incomplete by the design; an investigator might, for instance, choose in advance to take the measurements every hour on one half of the subjects and every two hours on other half.
In general, though, the major reason for the unbalanced data in a longitudinal study is occurrence of missing values in the sense that the intended measurements are not taken, are lost or are otherwise not available.
Half-normal plot is a plot for diagnosing the model inadequacy or revealing the presence of outliers, in which the absolute values of, for instance, the residuals from the multipl
Bivariate boxplot : A bivariate analogue of boxplot in which the inner area contains 50%of the data, and a 'fence' helps to identify the potential outliers. Robust methods or techn
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Mantel Haenszel estimator is an estimator of assumed common odds ratio in the series of two-by-two contingency tables arising from the different populations, for instance, occ
The statistical methods for estimation and inference which are based on a function of sample observations, probability distribution of which does not rely upon a complete speci?cat
The measure of the degree to which the particular model differs from the saturated model for the data set. Explicitly in terms of the likelihoods of the two models can be defined a
Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models. This will be very
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
A rule for computing the number of classes to use while constructing a histogram and can be given by here n is the sample size and ^ γ is the estimate of kurtosis.
Bayesian inference : An approach to the inference based largely on Bayes' Theorem and comprising of the below stated principal steps: (1) Obtain the likelihood, f x q describing
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