This historic book may have numerous typos and missing text.
Purchasers can download a free scanned copy of the original book
(without typos) from the publisher. Not indexed. Not illustrated.
Excerpt: ... standard error of estimate of principal regression
equations, by streams to a highly significant coefficient of +0.333
for log P in the second equation and to the fact that peak flow has
a very strong simple correlation with the quantity of water
discharge for a storm period. The significant regression
coefficients in table 8 are usually of the same variables that had
significant simple correlation with sediment concentration. See the
first section of table 6. Generally, the significance of a specific
regression coefficient is improved over that of the simple
correlation due to the effects of the multiple regression in
isolating the effects of a single variable. For example, the simple
correlation coefficients of concentration with time trend for the
Brandywine Creek is poorly significant (table 6); whereas, the
regression coefficients for time trend (table 8) are all highly
significant. In other instances the simple correlation coefficients
are not significant at the level tested, but the regression
coefficients are significant. The regression coefficients tend to
have the same sign as the simple correlation coefficients,
especially so if they are significant. Exception to this is noted
for rainfall quantity on the Brandywine Creek in which the simple
correlation coefficient is positive and highly significant;
whereas, the regression coefficients are negative and less than
highly significant. Intercorrelation among the independent
variables is the cause of this change of sign and significance.
Based on data used in this analysis and the equations derived
therefrom, water discharge proves to be the most important
independent variable for describing variation of sediment transport
in streams. The tendency is strong for the mean concentration of
|Country of origin:
||246 x 189 x 5mm (L x W x T)
||Paperback - Trade
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