Automated static covariance analysis with Mathematica

1990 ◽  
Author(s):  
L. F. Doty
Keyword(s):  
Covariance Analysis

Parent-offspring regression in meadow bromegrass (Bromus riparius Rehm.): Evaluation of two methodologies on heritability estimates

2004 ◽  
Vol 84 (1) ◽  
pp. 125-127
Author(s):  
Marcelo R. A. de Araujo ◽  
B. E. Coulman
Keyword(s):  
Regression Analysis ◽  
Block Design ◽  
Covariance Analysis ◽  
Regression Estimate ◽  
Regression Methods ◽  
Randomized Block Design ◽  
Bromus Riparius ◽  
Heritability Estimates ◽  
Analysis Error ◽  
Meadow Bromegrass

To determine the nature and extent of inflation of estimates of heritabilities by parent-offspring regression methods, 40 clones of meadow bromegrass (Bromus riparius Rehm.) and their half-sib progenies were studied in completely randomized block design trials, with six replications in Saskatoon and Melfort, Canada. Clones and progenies were evaluated for dry matter yield, seed yield, plant height, fertility index and harvest index. The results of the analysis showed a consistent inflation of heritability estimates derived from the simple parent-offspring regression, when compared to the regression estimate by variance-covariance analysis. The two methods successfully removed the environmental covariances from the estimates. However, in the simple regression analysis, error covariance was not removed from the numerat or; therefore, heritabilities estimated by this methodology were higher than those estimated by the variance-covariance method. It was concluded that estimates derived from variance-covariance analysis provide less biased estimates of heritability. Key words: Regression analysis, heritability, meadow bromegrass

Neutron induced cross section measurements on gallium isotopes at neutron energy 14.90 ± 0.01 MeV and covariance analysis

2019 ◽  
Vol 992 ◽  
pp. 121613 ◽  
Author(s):  
Rebecca Pachuau ◽  
B. Lalremruata ◽  
A. Gandhi ◽  
S.V. Suryanarayana ◽  
B.K. Nayak ◽  
...  
Keyword(s):  
Cross Section ◽  
Neutron Energy ◽  
Covariance Analysis

Determination of k and Q for 74Se, 113In, 186W and 191Ir targets applying covariance analysis

2019 ◽  
Vol 154 ◽  
pp. 108846 ◽  
Author(s):  
L.F. Barros ◽  
R.V. Ribeiro ◽  
M.S. Dias ◽  
M. Moralles ◽  
R. Semmler ◽  
...  
Keyword(s):  
Covariance Analysis

Length–Weight Relationship for Pomfret

1972 ◽  
Vol 29 (7) ◽  
pp. 1079-1081
Author(s):  
J. I. Manzer
Keyword(s):  
Size Range ◽  
Fork Length ◽  
Mesh Size ◽  
Gulf Of Alaska ◽  
Single Equation ◽  
Covariance Analysis ◽  
Allometric Equations ◽  
Pooled Data ◽  
Weight Relationship ◽  
Males And Females

The length–weight relationship for 298 pomfret (Brama japonica) ranging in fork length from 30 to 49 cm, and caught in 1956 and 1957 in the Gulf of Alaska by gillnets varying in mesh size from [Formula: see text] (63 mm) to [Formula: see text] inches (133 mm) was examined. Covariance analysis of the data indicated that within years the length–weight relationship did not differ between males and females; and, on the average, both males and females of a particular length were significantly heavier in 1956 than in 1957. Allometric equations for each year based on data for sexes combined are considered more appropriate than a single equation based on pooled data. The equations relating weight in kilograms to length in centimeters, for 1956 and 1957, respectively, are[Formula: see text]and apply to the lower third of the known size range for pomfret in the Gulf of Alaska.

Calibration of Experiments on Perennial Crops using Covariance Analysis: the Case of Coconut

1986 ◽  
Vol 22 (4) ◽  
pp. 353-361
Author(s):  
Tilak Abeysinghe
Keyword(s):  
Coefficient Of Variation ◽  
Experimental Error ◽  
Covariance Analysis ◽  
Mean Square ◽  
Perennial Crops ◽  
Randomized Design ◽  
Calibration Experiment ◽  
Experimental Yield ◽  
The Mean

SUMMARYThe calibrating efficiency of the pre-experimental yield of coconuts was examined using ten years data from a calibration experiment. On the basis of a fully randomized design it was found that the two-year pooled pre-experimental yield on four-tree plots produces consistent calibration and reduces the experimental error mean square by about 73%. This brings down the mean coefficient of variation to 9.7% from its pre-calibration levels of 36 on one-tree plots and 18 on four-tree plots.

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