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Juan Bernardo Pérez-Hernández and María José Grajal-Martín

determined at the end of the plantlet development phase. Quantitative results were subjected to partitioning of treatment sum of squares for statistical analysis, according to Little (1981) . Categorical results were analyzed through logistic regression

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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, 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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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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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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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