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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.
Chi-square analysis can be used with both Goodness-of-Fit Tests and with Tests for Independence. There are specific instances when each test should be used based on the information
how detect sources of error in sample survey
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