USING OF FACTOR ANALYSIS SCORES IN MULTIPLE LINEAR REGRESSION MODEL FOR PREDICTION OF KERNEL WEIGHT IN ANKARA WALNUTS

Abstract

Kernel weightis important for plantbreeders to select high productive plants. The determination of relationships betweenkernel weightand somefruit-kernel characteristics may providenecessary information forplantbreeders in selection programs. In thepresent study, the relationships between kernel weight(KW ) and7fruit-kernel characteristics : Fruit Length, (FL,), Fruit Width (FW) Fruit Height (FH) Fruit Weight (FWe) Shell Thickness (ST), Kernel Ratio (KR) and Filled-firm Kernel Raito (FKR,), were examined bythe combination of factor and multiple linear regression analyses.Firstly, factor analys is was used to reduce large number of explanatory variables, to remove multicolinearty problems and to simplify the complex relationships among fruit -kernel characteristics. Then, 3 factorshaving Eigen values greater than 1 were selected as independent or explanatory variables and 3 factor scores coefficients were used for