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Biplots: It is the multivariate analogue of the scatter plots, which estimates the multivariate distribution of the sample in a few dimensions, typically two and superimpose on these display representations of the variables on which the samples are measured and observed. The relationships amongst the individual sample points can be simply seen and they can also be related to the values of the measurements, thus making such plots useful for giving the graphical explanation of the data, for detecting patterns, and for giving the results found by more formal methods or techniques of analysis. Figure 16 shows the two-dimensional biplot of a set of multivariate data with 34 observations and 10 variables. Also see principal components analysis and corre- spondence analysis.
The estimator of the group by the time period interaction in a study in which the subjects in two different groups are observed in two different time periods. Normally one of th
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
Household interview surveys : The surveys in which the primary sampling units are typically geographic regions such as nations or cities. For each such unit sampled, there are addi
difference between histogram and historigram
The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional m
meaning,uses,shortcomings and drawbacks of vital statistics
how does it work exactly
Likelihood is the probability of a set of observations provided the value of some parameter or the set of parameters. For instance, the likelihood of the random sample of n observ
Minimization is the method or technique for allocating patients to the treatments in clinical trials which is usually the acceptable alternative to random allocation. The procedur
The function of a variable t which, when extended formally as a power series in t, yields factorial moments as the coefficients of the respective powers. If the P(t) is probability
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