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Sarah M. Smith and Zhanao Deng

.0 m in Expt. 1 and 15.2 m in Expt. 2, respectively. At these distances, the observed gene flow rate was 0.1% and 0.2%, respectively. Fig. 3. Scatterplots of observed gene flow rates in Expts. 1 and 2 at each distance measured and logistic regression

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Tyler C. Hoskins, James S. Owen Jr., Jeb S. Fields, James E. Altland, Zachary M. Easton, and Alex X. Niemiera

separately using logistic regression Eq. [1]: where a = rate of change and b = inflection point (RC = 0.5), assuming that RC will range from 0 to 1. Mean and se of the inflection point and rate of change estimates were reported. The resultant EC, NO 3

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Simon A. Mng’omba and Elsa S. du Toit

separation. Data on graft survival were analyzed using a generalized model (Proc GENMOD of the SAS system that performed logistic regression). Results Graft survival. There were significant differences ( P < 0.0001) in survival of grafted mango, avocado, and

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Melissa Broussard, Sujaya Rao, William P. Stephen, and Linda White

foraging honeybees and bumble bees were analyzed using R ( R Development Core Team, 2010 ) to create binomial logistic regression models for bee foraging behavior. Single-variable regressions were used to determine the correlation between wind speed and

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George C.J. Fernandez

.g., plant growth models) ( Schabenberger and Pierce, 2001 ). Categorical or qualitative responses need to be analyzed using categorical data analysis methods such as χ 2 test, logistic regression for binary response, and so forth. Spatial analysis is

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Hrvoje Rukavina, Harrison G. Hughes, and Yaling Qian

12 h and was turned off during the night. Clones’ survival was evaluated by observing shoot regrowth during a 2-month period. The experiment was analyzed as a completely randomized design with three replicates. The logistic regression procedure (proc

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Justin A. Schulze, Jason D. Lattier, and Ryan N. Contreras

comparison test was applied. Radicle and shoot emergence were analyzed using logistic regression models, with GA 3 , BA, and sucrose as the independent variables. Model parameters for radicle and shoot emergence data analyses were tested using a likelihood

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Christina M. Twardowski, Jaime L. Crocker, John R. Freeborn, and Holly L. Scoggins

of Variance Procedure of SAS and subjected to regression analysis using SAS General Linear Models Procedure (version 9.2; SAS Institute, Cary, NC). Rooting percentage data were transformed (arcsin), and analysis was performed by logistic regression

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Lakshmy Gopinath, Justin Quetone Moss, and Yanqi Wu

value of each genotype was determined using a logistic regression model using PROC PROBIT (SAS version 9.4; SAS Institute, Cary, NC) ( Qian et al., 2001 ; Shahba et al., 2003 ). The probit procedure generated a table of predicted percentage survival at

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Phillip M. Mohebalian, Mihaela M. Cernusca, and Francisco X. Aguilar

significantly different in terms of gender from consumers in segments 2 and 3 as denoted by (s2, s3) ( Table 2 ). The CA was analyzed using a conditional logistic regression ( Aguilar et al., 2009 , 2010 ; McFadden 1974 , 1986 ) and applied to each cluster to