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Eckhard Grimm and Moritz Knoche

of the bathing solutions. The fraction of cells plasmolyzing at the respective osmolarity was calculated as the first derivative of the logistic regression line depicted in the main graphs. When skin segments of ‘Hedelfinger’, ‘Sam’, and ‘Sweetheart

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Melody Reed Richards, Larry A. Rupp, Roger Kjelgren, and V. Philip Rasmussen

based on bud growth the following spring. Results of all experiments were analyzed using logistic regression tests of occurrence with Statistix 9 © (Analytical Software, Tallahassee, FL). Differences in least square means were completed in SAS

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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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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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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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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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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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Aude Tixier, Adele Amico Roxas, Jessie Godfrey, Sebastian Saa, Dani Lightle, Pauline Maillard, Bruce Lampinen, and Maciej A. Zwieniecki

described above. Statistical analysis. Data presented in Fig. 1 were analyzed with mixed effect logistic regression with treatment and date as fixed factors and trees as random factor. Data presented in Figs. 2 – 4 were analyzed with linear mixed effect