, vol. 1, issue 1, pp. 8-14, 2013.
Two field experiments were carried out in a commercial field at Abo
Rawash village, Giza governorate, Egypt during 2004 and 2005 seasons to
compare five statistical procedures including: simple correlation, path
analysis, multiple linear regression, stepwise regression and factor analysis
were in determining the relationship between sesame seed yield and its
contributing traits. Thirty sesame genotypes were used for this purpose.
The studied characters were: flowering date, plant height, stem height to
the first capsule, fruiting zone length, number of capsules on main stem,
number of capsules per plant, capsule density on main stem, 1000-seed
weight and seed yield per plant. The most important results can be
summarized as follows: 1. The simple correlation coefficients and path
analysis of yield components revealed that components with the highest
positive correlation to yield also had the highest positive direct effect to
yield i.e., number of capsules on main stem and number of capsules per
plant. Path analysis showed that, the residual effect (0.433) was high in
magnitude which shows that some other important yield contributing
characters which contribute to yield have to be included. 2. Stepwise
multiple regression analysis showed that 77.25% of the total variation in
seed yield could be explained by the variation in number of capsules per
plant and flowering date in sesame. The linear regression equation was (Y)
= 10.951 - 0.110 X1 + 0.114 X7, where Y, X1and X7 represent seed yield
per plant, flowering date and number of capsules per plant, respectively. 3.
Besides, coefficient of determination (R2), adjusted R-squared statistic and
standard error of estimate values, mean absolute error (MAE) and Durbin- Watson (DW) statistic test showed no significant differences between the
full model regression and stepwise multiple regression analysis technique.
However, the efficiency expressed is due to the reduction in number of
variables in the fitted model from all variables (full model regression) to
two variables only (stepwise multiple regression).
4. Factor analysis indicated that three factors could explain approximately
81.9% of the total variation. Factor analysis indicated that three factors
could explain approximately 81.9% of the total variation. The first factor
which accounted for about 41% of the variation was strongly associated
with fruiting zone length, number of capsules on main stem, number of
capsules per plant, and capsule density. The second factor which accounts
for about 25% of the variation, was strongly associated and positive effects
on days to flowering, 1000-seed weight, plant height and stem height to the
first capsule, whereas the third factor had positive effects on number of
fruiting branches only, which accounts for about 16% of the variation.
Factor analysis technique was more efficient than other used statistical
techniques. It provides more information about cluster of inter-correlated
variables. 5. Based on the five of statistical analysis techniques, agreed
upon that high yield of sesame plants could be obtained by selecting
breeding materials with high number of capsules on main stem, number of
capsules per plant, plant height and increasing capsule density on the main
stem.
