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Ada Baldi, Anna Lenzi, Marco Nannicini, Andrea Pardini, and Romano Tesi

emission spectroscopy (IRIS Intrepid II XSP Radial, Thermo Fisher Scientific). Data were analyzed separately for N, P, or K. Regression analysis was adopted and the coefficients of determination ( R 2 ) were calculated. When treatment effect was significant

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Clinton C. Shock, Erik B.G. Feibert, Alicia Rivera, Lamont D. Saunders, Nancy Shaw, and Francis F. Kilkenny

and harvest of the nonirrigated plants preceded the harvest of the irrigated plants ( Table 2 ). Seed was cleaned from stalks and chaff and was weighed. Seed yield means were compared by analysis of variance and linear and quadratic regression. Seed

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Tatiana Borisova and Pilar Useche

effect regression model. The regression model allows examining the changes in water use over time, and comparing participants’ water use before and after the workshop, as well as the water use of nonparticipants. The regression model is first estimated

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Benedict C. Posadas, Patricia R. Knight, Randal Y. Coker, Christine H. Coker, Scott A. Langlois, and Glenn Fain

estimated by Tobit method due to the limited range of values of some of the variables used in estimation ( Maddala, 1983 ). All the Tobit regression analyses were performed by using EViews 5 (Quantitative Micro Software, Irvine, CA). The descriptive

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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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Yuhung Lin and Yaling Qian

method ( Table 3 ). The Pearson correlation test was performed by Proc CORR to obtain Pearson statistic coefficients of individual minerals and its relationship with turf quality. Stepwise regression was conducted to determine whether any of the minerals

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Eugene K. Blythe and Jeff L. Sibley

277.9 ± 62.3 mg for Elaeagnus × ebbingei . There were 10 replications per treatment for each species. Data were analyzed using the GLM procedure of SAS (Version 9.2; SAS Institute Inc., Cary, NC). The resulting regression models were assessed using

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Charles E. Barrett, Lincoln Zotarelli, Lucas G. Paranhos, Peter Dittmar, Clyde W. Fraisse, and John VanSickle

trials in Northeast Florida were analyzed using correlation analyses to determine which weather parameters had the greatest effect on yield. Data were combined by production system and used to create regression equations that predicted yield based on

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Seong-Tae Choi, Doo-Sang Park, Seong-Mo Kang, and Soo-Jeong Park

determine total N, 0.2-g subsamples were analyzed with a Kjeldahl instrument (Kjeltec 2300; Foss Co., Höganäs, Sweden) by using the micro-Kjeldahl method ( Nelson and Sommers, 1973 ). Statistical analyses for regression and correlation were performed using

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Carmen Feller and Matthias Fink

.99 ºBrix·100 g·g −1 respectively; Fig. 3 ). Fig. 3. Refraction of pure fructose, glucose, sucrose, and refraction of dahlia fructans and chicory fructans related to solute concentration. Lines are regression lines. Lines for fructose and sucrose