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Xiaoli Ma, Xuefeng Liu, Pingwei Xiang, Shichun Qiu, Xiangcheng Yuan, and Mei Yang

grades of gummosis were analyzed. In addition, binary logistic regression analysis and ordinal logistics regression analysis through SPSS 18.0 were respectively applied to study the correlation between mineral elements and existence of the gummosis and

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Tanya J. Hall, Roberto G. Lopez, Maria I. Marshall, and Jennifer H. Dennis

dichotomous dependent variable, a binary logistic regression was used ( Liao, 1994 ). The logistic regression can be explained mathematically through the generalized linear model ( Liao, 1994 ). The econometric approach assumes an underlying response variable

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Julie M. Tarara, Paul E. Blom, Bahman Shafii, William J. Price, and Mercy A. Olmstead

functional relationships of expected responses to improve the potential for meaningful interpretation of TTM data in vineyards. Nonlinear regression analyses using logistic model forms were applied to produce average representations of canopy and fruit growth

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Jinshi Cui, Myongkyoon Yang, Daesik Son, Seongmin Park, and Seong-In Cho

regression is an appropriate statistical method for analyzing a binomial response and binary data. Logistic regression is a specific example of a generalized linear regression model in which the dependent variable or the observed response variable is first

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David W. Carey, Mary E. Mason, Paul Bloese, and Jennifer L. Koch

completed between December and May and scored between April and July (depending on graft date but after all the grafts in the set had either flushed or failed). Statistical analysis. Logistic regression analysis was used with graft outcome as the dependent

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Marlee A. Trandel, Penelope Perkins-Veazie, and Jonathan Schultheis

incidence of HH. An analysis of variance (ANOVA) was used to quantify cultigen differences in tissue firmness and HH severity. Logistic regression was used to evaluate the incidence of HH (HH%) and calculate cultigen predictive odds ratios for exhibiting HH

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Anna K. Kirk and Rufus Isaacs

distinguish possible non-linear relationships. Gaussian, gamma, and logistic nonlinear regression analyses were performed and parameters were compared among these non-linear curve types (PROC NLIN; SAS Institute Inc.). Although variations of each of these

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Omar A. Lopez, Danny L. Barney, Bahman Shafii, and William J. Price

. Statistical procedures. A logistic regression model was used previously to describe the seed germination process for V. membranaceum ( Shafii and Barney, 2001 ). This model provides parameter estimates for the cumulative germination percentage and speed of

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Tanya J. Hall, Jennifer H. Dennis, Roberto G. Lopez, and Maria I. Marshall

. Description of variables used in the logistic regression model. Results and Discussion Demographic characteristics of floriculture growers and operations. Growers ranged in age from 27 to 72 years old with a mean age of 57 years and were

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Rolston St. Hilaire, Dawn M. VanLeeuwen, and Patrick Torres

variables and a quantitative explanatory variable were explored using logistic regression. All computations were based on available data. Due to item nonresponse, fewer than 99 observations were available for some analyses. Significance was defined at P