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value of each genotype was determined using a logistic regression model using PROC PROBIT (SAS version 9.4; SAS Institute, Cary, NC) ( Qian et al., 2001 ; Shahba et al., 2003 ). The probit procedure generated a table of predicted percentage survival at

described above. Statistical analysis. Data presented in Fig. 1 were analyzed with mixed effect logistic regression with treatment and date as fixed factors and trees as random factor. Data presented in Figs. 2 – 4 were analyzed with linear mixed effect

significantly different in terms of gender from consumers in segments 2 and 3 as denoted by (s2, s3) ( Table 2 ). The CA was analyzed using a conditional logistic regression ( Aguilar et al., 2009 , 2010 ; McFadden 1974 , 1986 ) and applied to each cluster to

main effects (the treatments and the distance from the center of treatment injection) were tested using the logistic regression model which is specified as ln [ θ / ( 1 – θ ) ] = β 0 + β 1 D + β 2 S + β 3 SD + β 4 d, where 0 < θ < 1 denotes the

population. These markers can be useful for tagging agronomic traits using logistic regression and quantitative trait loci analysis. SSRs are abundant in plant genomes, easily transferable across species and across laboratories, codominantly inherited, and

1 was initiated on 31 May and Block 2 was initiated on 14 June 2014. Percentage germination and survival data were analyzed using analysis of variance (ANOVA) with logistic regression [PROC GLIMMIX (SAS version 9.3 for Windows; SAS Institute, Cary

growing season, and precipitation accounts for more than 90% of the variability in the incidence and severity of infection represented in a logistic regression model. The reliable detection of peach germplasm resistant to BLS with potential use in

= likely to engage or presence of intent). Conversion of the data aided in greater accuracy in interpretation ( Pasta, 2009 ). Following this conversion, we used a binary logistic regression model to estimate the relationship between perceived landscape

than normal distribution and, therefore, could not be analyzed using analysis of variance or a general linear model. Based on survival counts for each day, a logit model (logistic regression) was used to estimate the probability of survival and compute

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