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Darren J. Hayes and Bryan J. Peterson

statistical software R version 3.3.2 ( R Core Team, 2016 ). Propagation success was analyzed via logistic regression, with overall effects of treatments on propagation success analyzed by the Wald test conducted using the aod version 1.1-32 ( Lesnoff and

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Andrea N. Brennan, Valerie C. Pence, Matthew D. Taylor, Brian W. Trader, and Murphy Westwood

-squared test. A subsequent test using logistic regression, and a contrast of the log odds was used to determine which species had significantly different contamination rates. Explants affected by contamination were excluded from the growth and survival time

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Ryan J. Hill, David R. King, Richard Zollinger, and Marcelo L. Moretti

or 2,4-D. Table 2. Hazelnut sucker height in response to NAA and 2,4-D in field studies in Oregon in 2019 and 2020. Regression parameters for a four-parameter log-logistic regression: upper limit of height (max), time in growing degree days

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Nicole L. Russo, Terence L. Robinson, Gennaro Fazio, and Herb S. Aldwinckle

. Data were analyzed with logistic regression to determine likelihood of developing rootstock blight using a P value of 0.05. Based on the parameters of logistic regression, rootstock clones with no observed rootstock blight were excluded from analysis

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Mary Helen Ferguson, Christopher A. Clark, and Barbara J. Smith

logistic regression analysis to check for a possible predisposing effect of P. cinnamomi on X. fastidiosa infection was not significant ( P = 0.138). Ringspots were observed on 33% of X. fastidiosa –positive plants and 65% of X. fastidiosa –negative

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Te-Ming Tseng, Swati Shrestha, James D. McCurdy, Erin Wilson, and Gourav Sharma

recorded 4 weeks after treatment (WAT). PVN ratings were based on a 0 to 100% scale, with 0% corresponding to no visible necrosis and 100% corresponding to complete plant necrosis. Data were analyzed by rating date using a log-logistic regression technique

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Mokhles A. Elsysy and Peter M. Hirst

. Analysis was conducted using binomial logistic regression with mixed effects, with all treatments compared with control ( P < 0.05), using the statistical package R (version 3.2.2; R Foundation, Vienna, Austria). Gene transcript levels. Real

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Mokhles A. Elsysy, Michael V. Mickelbart, and Peter M. Hirst

. However, a binomial logistic regression with mixed-effects model was used to test flower formation. All data analyses were performed using R software version 3.2.2 (14 Aug. 2015) “Fire Safety” in the R statistical package (R Foundation, Vienna, Austria

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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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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