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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.
Historical controls : The group of patients treated in the past with the standard therapy, taken in use as the control group for evaluating the new treatment on the present patient
we are testing : Ho: µ=40 versus Ha: µ>40 (a= 0.01) Suppose that the test statistic is z0=2.75 based on a sample size of n=25. Assume that data are normal with mean mu and standa
what is pdf,mean & variance for multimodal distribution?
PRINCIPLES OF MODELLING IN OR.
Ha: If hyperlipidemia is believed to be a side effect of second-generation antipsychotics (SGAs), then Hispanic patients with SGAs treatment will have the higher frequency of devel
how to describe association between quantitative and categorical variables
Hanging rootogram is he diagram comparing the observed rootogram with the ?tted curve, in which dissimilarities between the two are displayed in relation to the horizontal axis,
The linear component ηi, de?ned just in the traditional way: η i = x' 1 A monotone differentiable link function g that describes how E(Yi) = µi is related to the linear compon
Quality-adjusted survival analysis is a method for evaluating the effects of treatment on survival which allows the consideration of quality of life as well as the quantity of lif
What is statistical inference? Statistical inference can be defined as the method of drawing conclusions from data which are subject to random variations. This is based o
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