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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.
Linear Regression Generally, in two mutually related statistical series, the regression analysis based on graphic method. Under graphic method the values of X and Y variable
Importance of official statistic
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Make a decision about the given claim. Use only the rare event rule, and make subjective estimates to determine whether events are likely. For example, if the claim is that a coi
Weighted Harmonic Mean Weighted Harmonic Mean is calculated with the help of the following formula: WHM Case
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Formation of Continuous Frequency Distribution: Continuous frequency distribution is most popular in practice. With reference to the formation of this type of frequency distr
Lifts usually have signs indicating their maximum capacity. Consider a sign in a lift that reads "maximum capacity 1400kg or 20 persons". Suppose that the weights of lift-users are
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