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Andreas Winkler, Stefanie Peschel, Kathleen Kohrs, and Moritz Knoche

relationship follows a sigmoidal pattern. A logistic regression model was fitted and the WU 50 calculated as the x coordinate at the point of inflection. Experiments. Potential relationships between the rate of water uptake and the T 50 and WU 50 were

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Laise S. Moreira and Matthew D. Clark

were dead or did not germinate) was compared among treatments using Pearson’s χ 2 test, binomial logistic regression in R package VCD (version 1.4-7) ( Meyer et al., 2020 ), and Fisher’s pairwise comparison tests with Bonferroni correction in R package

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Ockert Greyvenstein, Terri Starman, Brent Pemberton, Genhua Niu, and David Byrne

. Nominal logistic regression was performed on binomial data and differences were evaluated based on odds ratios. Results Experiment 1: Preliminary investigation to detect differences between clones. Clones ‘Belinda’s Dream’ (BD), ‘Basye’s Blueberry’ (BB

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Thomas Björkman and Stephen Reiners

logistic regression, allowing the treatment intensities and ratings to be ordinal values rather than continuous. Table 1. Characteristics of western New York snap bean fields tested for response to starter phosphorus (P) treatment and/or potassium

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McKenzie Thomas, Kimberly Jensen, Margarita Velandia, Christopher Clark, Burton English, Dayton Lambert, and Forbes Walker

. Six measurement logistic regressions relate the indicator variables (use of the six ecofriendly practices), and the latent variable, Ecopract . The probabilities of selecting each ecofriendly gardening practice are as follows: Pr ( P o l l i n a t o r

Open access

Lauren Fessler, Amy Fulcher, Liesel Schneider, Wesley C. Wright, and Heping Zhu

. To test whether there were differences in probability for powdery mildew, a multivariable mixed logistic regression model was developed using PROC GLIMMIX with a binary distribution and logit link. The fixed effects tested included sprayer type

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Suzanne O’Connell

response variable related to the percentage of the crop that was nonmarketable was analyzed differently because it was not a continuous variable. This variable was analyzed with a logistic regression model to predict a proportion of the total harvested

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identification of plants possessing a resistant reaction to SPVD. AMOVA analysis found significant ( P < 0.002) variation between the two phenotypic groups using 206 polymorphic AFLP markers. Discriminant analysis and logistic regression statistical methods were