We can also use the computing formula that avoids tak-ing individual deviations: and Figure 20-19 shows two distributions having the same mean but different standard deviations (curves A and B) and two distributions having the same standard devia-tion but different means (curves A and C). The mean and the variance of a distribution do not describe it completely. They do not distinguish a sym-metrical distribution from an asymmetrical one, for ex-ample. There are even symmetrical distributions that have the same mean and variance but still have some-what different shapes. Nevertheless, for the purposes of dealing with most quantitative genetic problems, the mean and variance sufﬁce to characterize a distribution. Measures of relationship Covariance and correlation Another statistical notion that is of use in the study of quantitative genetics is the association, or correlation, between variables. As a result of complex paths of causation, many variables in nature vary together but in an imperfect or approximate way. Figure 20-20a provides an example, showing the lengths
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This note was uploaded on 01/10/2011 for the course BIOL BIOL taught by Professor Johnson during the Spring '08 term at Aberystwyth University.