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Correlation matrix: A square, symmetric matrix with the rows and columns corresponding to the variables, in which the non diagonal elements are correlations between the pairs of the variables, and elements on the major diagonal are unity. An instance for measures of muscle and body fat is given below
V1 = tricep(thickness mm), V2 = thigh(circumference mm
V3 = midarm(circumference mm), V4 = bodyfat(%).
The method of displaying the geographical variability of the disease on maps using different colors, shading, etc. The logic is not new, but the arrival of computers and computer g
Linked micro map plot is a plot which provides the graphical overview and the details for spatially indexed statistical summaries. The plot shows the spatial patterns and statisti
Briefly explain the importance of forecasting for managers?
Hazard function : The risk which an individual experiences an event in a small time interval, given that the individual has survived up to the starting of the interval. It is th
Battery reduction : A common term for reducing the number of variables of the interest in a study for the purposes of study and perhaps later data collection. For instance, an over
There is high level of fluctuation in a zigzag pattern in the time series for RESI1 which indicates that there is possibly negative autocorrelation present. Column C11 show
Profile plots is a technique of representing the multivariate data graphically. Each of the observation is represented by a diagram comprising of a sequence of equispaced vertical
It is used generally for the matrix which specifies a statistical model for a set of observations. For instance, in a one-way design with the three observations in one group, tw
Back-projection: A term most often applied to the procedure for reconstructing plausible HIV incidence curves from the AIDS incidence data. The method or technique assumes that th
Catastrophe theory : A theory of how little is the continuous changes in the independent variables which can have unexpected, discontinuous effects on the dependent variables. Exam
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