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Analysis of variance allows us to test whether the differences among more than two sample means are significant or not. This technique overcomes the drawback of the method used in statistical inferences, which allows us to test the significance among the means drawn from two populations only. Since managers are required to test the significance of the differences among the means and variances drawn from more than two populations, the importance of this technique cannot be underestimated. Therefore it is natural that this technique plays an important role in the day-to-day decision making. We do observe a certain degree of similarity between this technique and the Chi Square test (employed for testing significance of proportions among more than two populations) which we have seen earlier.
A suitable example for ANOVA would be to compare the stipend given to the management graduates belonging to various premier institutes during their summer internship. If we would set up a hypothesis to test the significance of the differences among the means, it would be like
Cause and Effect Even a highly significant correlation does not necessarily mean that a cause and effect relationship exists between the two variables. Thus, correlation does
how to compute reliability coefficient for extracted factors in factor analysis?
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Multivariate analysis of variance (MANOVA) is a technique to assess group differences across multiple metric dependent variables simultaneously, based on a set of categorical (non-
Normal Distribution Meaning: According to ya Lun Chou There perfectly smooth and symmetrical curve, resulting from the expansion of the binomial (p+q) n when n approac
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