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Rui Wang, Yuqing Gui, Tiejun Zhao, Masahisa Ishii, Masatake Eguchi, Hui Xu, Tianlai Li, and Yasunaga Iwasaki

buds; a and b are logarithmic function parameters; and y is the number of floral buds. In addition, the results of logistic regression analysis were expressed for the growth of total dry matter and leaf area. The parameters in this logistic

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Travis Robert Alexander, Carolyn F. Ross, Emily A. Walsh, and Carol A. Miles

differences between the levels of main factors and interactions for significant attributes. A logistical regression model was used for the analysis of categorical data; Fisher’s exact test and chi-square test were carried out to determine nonrandom

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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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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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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

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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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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