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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

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

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

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

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

= 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

using logistic regression and the distribution of seedlings for different vigor categories (based on plant height) were compared using an ordinal regression, also examining the factorial combination of female parent and male parent. Herbarium voucher

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

. For the disease data, ordinal logistic regression was performed and parameter median by treatment and year reported. Results and Discussion Berry yield. Yield was significantly impacted by shade and year ( Fig. 1 ). In all years, yield decreased with

, B.M. 2000 Logistic regression analysis of factors influencing core breakdown in ‘Conference’ pears Postharvest Biol. Technol. 20 1 25 37 doi: 10.1016/S0925-5214(00)00114-9 Mditshwa, A. Magwaza, L.S. Tesfay, S.Z. 2019 Shade netting on