. Carnegie Institution of Washington publication. POSITION REGRESSION—PRIMARY BRANCHES. 61 Pearson (loc. cit.) has shown that this expression is sufficiently ac- curate for all ordinary purposes, and it is much easier to calculate than the complete expression for the standard deviation of v. Table 33.—Correlation between leaf-number and position of primary-branch whorls.. We see at once from table 33 that— (1) There is a very considerable degree of correlation between the number of leaves per whorl and the position of the whorl. (2) The degree of correlation is very closely the same for all se

- Image ID: RFWBDP
. Carnegie Institution of Washington publication. POSITION REGRESSION—PRIMARY BRANCHES. 61 Pearson (loc. cit.) has shown that this expression is sufficiently ac- curate for all ordinary purposes, and it is much easier to calculate than the complete expression for the standard deviation of v. Table 33.—Correlation between leaf-number and position of primary-branch whorls.. We see at once from table 33 that— (1) There is a very considerable degree of correlation between the number of leaves per whorl and the position of the whorl. (2) The degree of correlation is very closely the same for all se
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Image ID: RFWBDP
. Carnegie Institution of Washington publication. POSITION REGRESSION—PRIMARY BRANCHES. 61 Pearson (loc. cit.) has shown that this expression is sufficiently ac- curate for all ordinary purposes, and it is much easier to calculate than the complete expression for the standard deviation of v. Table 33.—Correlation between leaf-number and position of primary-branch whorls.. We see at once from table 33 that— (1) There is a very considerable degree of correlation between the number of leaves per whorl and the position of the whorl. (2) The degree of correlation is very closely the same for all series. We should expect, of course, that Series V and VI would give different values for the coefficients, because in those cases we are dealing with three and four different orders of branches together. (3) There can be no doubt that the regressions are not linear. The differences between v and r are so considerable that I have not thought it necessary to work out the probable errors for every case. The series which gives the smallest difference between rand ^ is V (>?—r=0.0197), but the apparent approach to linearity here is due to putting different orders of branches together. Considering the primary branches alone, the minimum difference between v and r is given by Series I (17 — r = 0.1142). We may take these two instances as a sample: It has been shown by Blakeman (loc. cit.) that if we let an approximate formula for the probable error of ^, i. e., E^^ is C Vn E^ 0.67449 il/f \n+(i-v'y-(i-ry Working from this formula we have for Series I, and for Series V, C = 0.1364 ±0.0193, ^ = 0.0191 ±0.0082.. Please note that these images are extracted from scanned page images that may have been digitally enhanced for readability - coloration and appearance of these illustrations may not perfectly resemble the original work.. Carnegie Institution of Washington. Washington, Carnegie Institution of Washington

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