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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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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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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

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Arthur Villordon, Ron Sheffield, Jose Rojas and Yin-Lin Chiu

outcome; an accurate classifier should have an AUC of more than 0.50 ( Fawcett, 2006 ). These values were calculated using Hugin Researcher's analysis wizard. The predictive accuracy of the candidate BBN models was compared with logistic regression

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Arthur Villordon, Craig Roussel and Tad Hardy

The sweetpotato weevil [SPW, Cylas formicarius (Fabricius)] is an important economic pest in “pink-tagged” or SPW-infested areas of Louisiana. From time to time, sweetpotato weevils are detected in “green-tagged” or SPW-free locations. When sweetpotato weevils are detected in “green'tagged” areas, the produce is quarantined and may not be shipped to locations that do not allow “pink-tagged” sweetpotatoes. As part of the statewide SPW monitoring program, the Louisiana Department of Agriculture and Forestry (LDAF) conducts a statewide pheromone-based trapping program to monitor SPW presence in beds and fields. We used SPW presence-absence data with a GIS-based logistic regression modeling tool to assess the feasibility of developing a model for predicting SPW risk in sweetpotato beds. Using pheromone trap data from 2001–03, we performed stepwise logistic regression experiments to assess the role of various weather variables (daily mean maximum and minimum temperature, rainfall) in the occurrence of SPW in beds. Our modeling experiments showed a strong relationship of mean daily minimum temperature during the winter months with SPW occurrence in beds. In particular, a logistic regression equation developed from 2003 trap data and mean April daily minimum temperature created a spatially accurate map of SPW risk for 2002. However, the same model did not accurately predict the 2001 SPW risk. These results indicate that additional variables are needed to improve the predictive ability of the model. Spatial risk mapping can be a potentially useful tool for decision makers in choosing between risk-averse and -prone decisions.