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Luke Miller, George Vellidis and Timothy Coolong

were wrapped in polyethylene bags and measured within 1 min of sampling. Data were subjected to the general linear model (GLM) procedure and mean separation using Tukey’s honest significant difference test ( P < 0.05) with SAS statistical software

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Alicain S. Carlson and John M. Dole

linear model (GLM) procedure and means separated by Tukey’s multiple comparison procedure at α = 0.05 using SAS (version 9.3; SAS Institute, Cary, NC). The crate or plot was used as the experimental unit. For long-term planting density data were analyzed

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Waltram Second Ravelombola, Ainong Shi, Yuejin Weng, John Clark, Dennis Motes, Pengyin Chen and Vibha Srivastava

linear model (GLM) procedure of JMP Genomics 7 (SAS Institute, Cary, NC). The mean separation was performed using the Student’s t test at alpha = 0.05. The descriptive statistics were generated using “Tabulate”; the correlations among the parameters

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Ah-Chiou Lee, Fang-Shin Liao and Hsiao-Feng Lo

temperature swings. Thus, breeding high-temperature-tolerant cultivars with late bolting is a high priority for mitigating the effects of global warming and climate changes. Table 5. Multiple regression analysis using general linear models (GLMs) for the

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Peter L. Sholberg and Paul Randall

interactions between main effects identified with the general linear models (GLM) procedure (SAS Institute, Cary, N.C.), and means were separated using the least significant difference ( lsd ) test ( P = 0.05). When a significant interaction occurred between

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Lijing Zhou, David L. Creech, Ken W. Krauss, Yin Yunlong and David L. Kulhavy

weight) as a measure of the capacity for individual plant performance under the different experimental treatments. Data analysis. The General Linear Model (GLM) procedure of SAS ( SAS Institute, Inc., 2007 ) was used to detect significant differences in

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Marisa M. Wall, Kate A. Nishijima, Lisa M. Keith and Mike A. Nagao

packaging experiments, destructive quality data were subjected to analysis of variance using the general linear models (GLM) procedure ( SAS Institute, 2002 ) for a randomized complete block design with four blocks for each treatment. A block consisted of

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Betsey Miller, Denny J. Bruck and Vaughn Walton

-zero reference data, we compared mean total shoot length, dry shoot weight, total leaf surface area, and dry root weight between cultivars using General Linear Model (GLM) analysis of variance (ANOVA). In addition, we performed a linear regression analysis to

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Christy T. Carter and Catharine M. Grieve

-effects general linear model (GLM) analysis of variance (ANOVA) was used to determine the effects of water composition (CCRW or SWD) and salinity on mineral concentration and growth parameters for each variety. When differences were found, a Tukey post hoc

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Virender Kumar, Daniel C. Brainard and Robin R. Bellinder

Wiley mill to pass a 2-mm screen. Ground litter at each sampling date was analyzed for nitrogen using a LECO CN-2000 (St. Joseph, MI). Statistical analysis. Data were subjected to analysis of variance and analyzed using the general linear model (GLM