Multiple discriminant analysis

multiple discriminant analysis example

The nature of the independent variables is categorical in Analysis of Variance ANOVAbut metric in regression and discriminant analysis. As in statistics, everything is assumed up until infinity, so in this case, when the dependent variable has two categories, then the type used is two-group discriminant analysis.

Only two variables are shown here, however, multiple variables are usually present. This simplifies the other differences among the stocks without totally dismissing them.

On the other hand, in the case of multiple discriminant analysis, more than one discriminant function can be computed. DFs are orthogonal in the multivariate space described by all variables in the analysis. Findings — The findings confirmed financial ratios as good classification and predictor variables of firm's performance.

Explanatory variables are continuous. Multiple discriminant analysis is also known, at least to statisticiansas canonical variates analysis or canonical discriminant analysis.

If the mean of a variable is correlated with its variance, significance tests may be invalid. Construction and evaluation of multiple discriminant functions is more likely and may require greater sampling effort more objects to achieve significance.

Similarly, there are some similarities and differences with discriminant analysis along with two other procedures.

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Multiple Discriminant Analysis