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Least squares linear regression analysis .was used to <br />statistically determine the relationship between peak May and <br />June flows and total annual flow for the Gunnison River for the <br />pre-dam and post-dam periods of record. We divided the data set <br />into pre-dam and post-dam for this analysis and present the <br />results in Figure 7. All regression lines are all statistically <br />significant at P=.0000 and ail have RZ values greater than 0.70, <br />indicating that the lines explain a large portion of the <br />variation in the data set. <br />`.~ 40000 <br />T <br />U <br /> <br />O <br />z <br />0 <br />~_ <br />X <br />Q <br />30000 <br />20000 <br />10000 <br />0 <br />- May (1897-1965), R2=.71, p=.0000 <br />---- June (1897-1965), R2=.73, p=.0000 <br />-- May (1966-1992), R2=.84, p=.0000 <br />- - June (1966-1992), R2=.7i, p=.0000 <br /> <br />,; -_ <br />. ,`,- . <br />_ ,. , <br />,, , <br />,,,, <br />,. . <br />_ ,. ,, <br />,. , <br />,_,- - <br />~; <br />,- <br />. ,_ <br />,- -~ <br />~~; <br />~- <br />1000000 2000000 3000000 4000000 <br />TOTAL ANNUAL FLOW (ac-ft) <br />Figure 7. Regression analyses of maximum May-and June flows vs. <br />total annual flow. The data set is divided into pre-dam and <br />post-dam data sets. <br />12 <br />