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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.
1. Use the concepts of sampling error and z-scores to explain the concept of distribution of sample means. 2. Describe the distribution of sample means shape for samples of n=36
who invented the chi square test and why? what is central chi square and non central chi square test? what is distribution free statistics? what are the conditions when the chi squ
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