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Timothy L. Righetti, David R. Sandrock, Bernadine Strik, Carmo Vasconcelos, Yerko Moreno, Samuel Ortega-Farias, and Pilar Bañados

treatments (narrow and wide) in this hypothetical horticultural experiment ( Fig. 2A ). All points fall on the same regression line, regardless of in-row spacing. Because plants at wide or narrow in-row spacing have a different dry weight ( Fig. 2A

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Eugene K. Blythe and Donald J. Merhaut

studies have involved the use of linear models or special cases of linear models, such as analysis of variance (ANOVA) or linear regression analysis, for statistical analysis of sample data. Linear models, including ANOVA and linear regression models

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Carlo Fallovo, Valerio Cristofori, Emilio Mendoza de-Gyves, Carlos Mario Rivera, Roberto Rea, Simone Fanasca, Cristina Bignami, Youssef Sassine, and Youssef Rouphael

-COR, Lincoln, NE) calibrated to 0.01 cm 2 . The relationships were evaluated by fitting regression models with the linear regression procedure of SPSS (SPSS Inc., Chicago, IL) and the stepwise elimination option as reported by Miranda and Royo (2003a) . The

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Julie M. Tarara, Paul E. Blom, Bahman Shafii, William J. Price, and Mercy A. Olmstead

functional relationships of expected responses to improve the potential for meaningful interpretation of TTM data in vineyards. Nonlinear regression analyses using logistic model forms were applied to produce average representations of canopy and fruit growth

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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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Edgar L. Vinson III, Floyd M. Woods, Joseph M. Kemble, Penelope Perkins-Veazie, Angela Davis, and J. Raymond Kessler

contrast were used to test simple effects at P = 0.05. Simple linear regressions relating SS:TA, pH, and SS to tendril number, watermelon circumference and weight, and CIELAB coordinates were performed using PROC MIXED, and the model fits were determined

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Yung-Kun Chuang, I-Chang Yang, Chao-Yin Tsai, Jiunn-Yan Hou, Yung-Huei Chang, and Suming Chen

the input for spectral analysis. Two standard multivariate analysis methods, modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR), were used to explore the relationships between the reflectance spectra and

Open access

Marlee A. Trandel, Penelope Perkins-Veazie, and Jonathan Schultheis

across the years. Linear regression was used to determine if there was a linear relationship between tissue firmness and incidence of HH. Coefficient of determination ( R 2 ) values were also used to assess the relationship between tissue firmness and

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Neil O. Anderson, Mi-kyoung Won, and Dong-chan Kim

values and water bath temperature treatment R 2 were determined with regression analyses for each temperature/photoperiod treatment (LTSD, LTLD, HTSD, and HTLD) using Sigma Plot (v. 8; SPSS Inc.). The inflection point or midpoint of the sigmoid response

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Mark K. Ehlenfeldt, Lisa J. Rowland, Elizabeth L. Ogden, and Bryan T. Vinyard

, Inc., 2005 ). Table 1 lists the number of LT 50 value estimates used in subsequent analysis of variance (ANOVA) and regression analyses. Some LT 50 estimates were omitted because the observed data did not visually indicate that a probit model was