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Research Paper | May 15, 2014

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Multiple regression analysis for studied traits in hormonal seed priming of sesame (Sesamum indicum) cultivars

Reza Farhadi Atalou, Bahram Mirshekari, Mohammad Naghi Safar Zadeh Vishkaei

Key Words: Dependent variable, Orthogonal comparison, Stepwise regression analysis

Int. J. Biosci.4( 10), 1-5, May 2014

DOI: http://dx.doi.org/10.12692/ijb/4.10.1-5


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In order to study multiple regression analysis for studied traits in sesame (Sesamum indicum) cultivars greenhouse and field experiments were conducted during 2012-2013 in Islamic Azad University, Tabriz Branch, Iran. Treatments were GA and Kinetin at five concentrations of 0, 50, 100, 150 and 200 ppm. To formulate the relationship among five independent growth variables measured in our experiment for sesame crop with a dependent variable, multiple regression analysis was carried out for the root bulk, capsule number per plant, capsule length, 1000 seed weight and seed yield; and oil yield as a dependent variable. Also, the stepwise regression analysis was carried out for the data obtained to test the significance of the independent variables affecting the oil yield. Based on orthogonal comparison results there is no significant difference between two sesame cultivars. The stepwise regression analysis verified that the root bulk, capsule number per plant and seed yield had a marked increasing effect on the sesame oil yield.


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Multiple regression analysis for studied traits in hormonal seed priming of sesame (Sesamum indicum) cultivars

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