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Multivariate analysis involves a set of techniques to analyse data sets on more than one variable. Many of these techniques are modern and often involve quite sophisticated use of computing tools. such analyses refer to all statistical methods that simultaneously analyse multiple measurements on each individual or object under investigation. Hence, any analysis simultaneously involving analysis of more than or equalto two variables can loosely be considered multivariate analysis.
This unit will provide a list of such analyses ill order to help decide when to use a given statistical technique for a given type of data or statistical question. It also gives a brief description of each technique. It is organized according to the number of data sets to analyze: one or two (or more). With two data sets we consider two cases: in the first case, one set of data plays the role of predictors or independent.variablesand the second set of data corresponds to measurements or dependent variables; in the second case. the different sets of data correspond to different sets of dependent variables.
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Simple Linear Regression While correlation analysis determines the degree to which the variables are related, regression analysis develops the relationship between the var
Q. Find the inverse Laplace transform of Y (s) = s-4/s 2 + 4s + 13 +3s+5/s 2 - 2s -3. Q. Use the Laplace transform to solve the initial value problem y''+ y = cos(3t), y(0) =
how to compute reliability coefficient for extracted factors in factor analysis?
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A medical researcher has 100 bone cancer patients in a study. Every patient's condition is one of six types, type \A" to type \F". The 100 patients split as follows: x There
The PCA is amongst the oldest of the multivariate statistical methods of data reduction. It is a technique for simplifying a dataset, by reducing multidimensional datasets to lower
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