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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, 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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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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M. Mcharo, D. LaBonte, R.O.M. Mwanga, and A. Kriegner

Molecular markers linked to resistance to sweetpotato chlorotic stunt closterovirus [SPCSV (genus Crinivirus, family Closteroviridae)] and sweetpotato feathery mottle virus [SPFMV (genus Potyvirus, family Potyviridae)] were selected using quantitative trait loci (QTL) analysis, discriminant analysis and logistic regression. Eighty-seven F1 sweetpotato [Ipomoea batatas (L.) Lam.] genotypes from a cross of `Tanzania' and `Wagabolige' landraces were used to generate DNA marker profiles for this study. Forty-five of the clones were resistant to SPCSV while 37 were resistant to SPFMV. A combination of 232 amplified fragment length polymorphism (AFLP) markers and 37 random amplified polymorphic DNA (RAPD) markers obtained were analyzed to determine the most informative markers. All three statistical procedures revealed that AFLP marker e41m33.a contributed the greatest variation in SPCSV resistance and RAPD marker S13.1130 accounted for most of the variation in SPFMV resistance. The power of discriminant and logistic analyses is that you do not need a parent-progeny population. An evaluation of these two models indicated a classification and prediction accuracy rates of 96% with as few as four markers in a model. Both multivariate techniques identified one important discriminatory marker (e44m41.j) for SPCSV and two markers (e41m37.a and e44m36.d) for SPFMV that were not identified by QTL analysis.

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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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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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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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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

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Maria C. Morera, Paul F. Monaghan, Michael D. Dukes, Ondine Wells, and Stacia L. Davis

survey respondents with ET and SMS controllers. Logistic regression analysis in SPSS was used to identify factors associated with the likelihood of continuing to use ET and SMS controllers after the completion of the project. This was done to judge