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Theekshana C. Jayalath, George E. Boyhan, Elizabeth L. Little, Robert I. Tate, and Suzanne O’Connell

variables. This included percent nonmarketable, physiological defect categories (i.e., bolting, tip burn, and undersized heads) and percent dead plants due to S. sclerotiorum infection. Each variable was analyzed separately with a logistic regression model

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Maren E. Veatch-Blohm and Lindsay Morningstar

–2009). The proportion of plants that emerged and flowered when the salinity treatments were applied pre-emergence was analyzed using ordinal logistic regression in the fit model platform of JMP 8 (SAS Institute Inc., 1989–2009). Results Soil effluent

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Katherine M. Solo, Sara B. Collins, Madalyn K. Shires, Ron Ochoa, Gary R. Bauchan, Liesel G. Schneider, Alan Henn, James C. Jacobi, Jean L. Williams-Woodward, M.R. Hajimorad, Frank A. Hale, John B. Wilkerson, Alan S. Windham, Kevin L. Ong, Mathews L. Paret, Xavier Martini, David H. Byrne, and Mark T. Windham

. Plants that were symptomatic were further analyzed for RRV by TaqMan RT-qPCR. Multilevel logistic regression was performed to test factors associated with the probability of detecting RRV from tested plants. Manual forward model selection was used to

Open access

Suzanne O’Connell and Robert Tate

analyzed differently because they comprised small positive integers that were not continuous. A logistic regression model was used to predict the proportion of the total harvested heads that were non-marketable using planting date or cultivar type as

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Sahar Dabirian, Debra Inglis, and Carol A. Miles

transformation was most appropriate in both cases). Multivariable logistic regression (logistic mixed model), with the three fixed effects (location, mulch, and rootstock) and a random effect for main plot, nested within the location–mulch combination in SAS

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Yan Chen, Regina P. Bracy, Allen D. Owings, and Joey P. Quebedeaux

's significant difference test at P < 0.05 was used to separate means. Ordinary linear regression models (PROC REG) were used to determine the response of SI, DW, and leaf tissue N concentration to GF rates. Exact logistic regression (PROC LOGISTIC) was used to

Open access

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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Suzanne O’Connell

analyzed separately to account for different weather patterns. The response for variable, nonmarketable yield was analyzed differently because it was comprised of small positive integers that were not continuous. A logistic regression model was used to

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Heidi A. Kratsch, Ruby Ward, Margaret Shao, and Larry A. Rupp

-Mann-Whitney (two samples) or Kruskal Wallis tests (greater than two samples) for ordinal dependent variables ( University of California, Los Angeles, 2007 ). Spearman rank correlation was used to investigate relationships between ordinal variables. Simple 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