2015
DOI: 10.1614/ws-d-13-00159.1
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Research Methods in Weed Science: Statistics

Abstract: There are various reasons for using statistics, but perhaps the most important is that the biological sciences are empirical sciences. There is always an element of variability that can only be dealt with by applying statistics. Essentially, statistics is a way to summarize the variability of data so that we can confidently say whether there is a difference among treatments or among regression parameters and tell others about the variability of the results. To that end, we must use the most appropriate statist… Show more

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Cited by 59 publications
(49 citation statements)
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“…For simplicity, we have restricted attention here to polynomial models, but the general principles, including lack-of-fit testing, can also be applied to intrinsically non-linear models with or without asymptote such as the sigmoidal growth curves (logistic, Gompertz, etc.) (Schabenberger & Pierce, 2002;Chapter 5;Ritz, Kniss, & Streibig, 2015). Also, one may apply a generalized linear model framework (Lee et al, 2006;McCullagh & Nelder, 1989), where inverse polynomials provide a particularly convenient way to model non-linear relationships, also when the treatment design entails qualitative factors.…”
Section: Discussionmentioning
confidence: 99%
“…For simplicity, we have restricted attention here to polynomial models, but the general principles, including lack-of-fit testing, can also be applied to intrinsically non-linear models with or without asymptote such as the sigmoidal growth curves (logistic, Gompertz, etc.) (Schabenberger & Pierce, 2002;Chapter 5;Ritz, Kniss, & Streibig, 2015). Also, one may apply a generalized linear model framework (Lee et al, 2006;McCullagh & Nelder, 1989), where inverse polynomials provide a particularly convenient way to model non-linear relationships, also when the treatment design entails qualitative factors.…”
Section: Discussionmentioning
confidence: 99%
“…B. subalternans percentage of control was visually evaluated at 21 days after treatment (DAT), using a scale of 0 to 100%, in which 0% represents no control, and 100% represents plant death. Data were subjected to analysis of variance (ANOVA) and the four‐parameter nonlinear logistic regression model was fitted using the drc package in R software version 3.5.1 (R Core Team, Vienna, Austria) . The dose providing 50% response (LD 50 ) was determined and the RF was calculated as the ratio of LD 50 between the GR and GS biotypes.…”
Section: Methodsmentioning
confidence: 99%
“…Data were subjected to analysis of variance (ANOVA) and the four-parameter nonlinear logistic regression model was fitted using the drc package in R software version 3.5.1 (R Core Team, Vienna, Austria). 16 The dose providing 50% response (LD 50 ) was determined and the RF was calculated as the ratio of LD 50 between the GR and GS biotypes.…”
Section: Glyphosate Dose-response and Effect Of Low Temperaturesmentioning
confidence: 99%
“…The relationship between spring wheat grain yield loss and wild oat density was simulated using rectangular hyperbolic function described by Cousens (1985) and Ritz et al (2012). To fit the rectangular hyperbolic curve drm function from the package drc was used, specifying the two-parameter Michaelis-Menten as the model, then function yieldLoss was used to estimate the parameters I and A.…”
Section: Methodsmentioning
confidence: 99%