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

explained by the seven independent variables included in the total revenues empirical model. Table 5. Tobit results of total revenues, annual workers' earnings, number of full-time equivalent workers, and total man-hours employed in nurseries and greenhouses

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R. Karina Gallardo, Diem Nguyen, Vicki McCracken, Chengyan Yue, James Luby, and James R. McFerson

clusters whose mean values differed significantly. We used a double-bounded Tobit model to investigate factors significantly influencing breeders’ selection likelihood. A single (pooled) model for the nine trait clusters combined is appropriate when the

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Alba J. Collart, Marco A. Palma, and Charles R. Hall

function ( Pazgal et al., 2005 ). Gujarati (1995) acknowledges that the four most commonly used discrete choice models derived from RUT are the Linear Probability Model, the Logit model, the Probit model, and the Tobit or censored regression model. In

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Chengyan Yue, Jingjing Wang, Eric Watkins, Stacy A. Bonos, Kristen C. Nelson, James A. Murphy, William A. Meyer, and Brian P. Horgan

factors that influence the ratings of breeders, we used double-bounded Tobit models (ratings range, 0 to 10; left-censored at 0 and right-censored at 10). An unobservable underlying latent variable ( ${\it Y}^ * $ ) was used to express the observed

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Chengyan Yue, Jingjing Wang, Eric Watkins, Stacy A. Bonos, Kristen C. Nelson, James A. Murphy, William A. Meyer, and Brian P. Horgan

employed a double-bounded Tobit model to investigate the factors significantly influencing breeders’ and distributors’ likelihood of trait selection. The observed dependent variables were the likelihood of selecting the trait clusters (the likelihood range

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Gregory E. Frey, Tarik Durmus, Erin O. Sills, Fikret Isik, and Marcus M. Comer

many logs did not produce any mushrooms, we used a censored (lower limit 0), linear Tobit regression model with a two-part likelihood function, appropriate for a dependent variable that represents both the probability of production and the amount of

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Giuseppe Timpanaro, Arturo Urso, and Vera T. Foti

not inferior to those of the model with constant returns to scale. On completion of this investigation, the indicators of efficiency were used as a dependent variable in a Tobit regression model, proposed originally by James Tobin (1958) for describing

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Chengyan Yue, R. Karina Gallardo, Vicki A. McCracken, James Luby, James R. McFerson, Lan Liu, and Amy Iezzoni

study. The 34 complete responses represent a 57% response rate. We conducted t tests to evaluate if breeder ratings differed significantly between the considerations/parties for each of the analyzed survey questions. We used double-bounded Tobit models

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Benjamin L. Campbell and William Steele

data can result in biased results. Thereby, censoring was accounted for within the data by using the two-limit Tobit model developed by Rossett and Nelson (1975) . The model can be represented as y i * = β ′ x i + ε i ( i   =   1,   .   .   .   ,   n

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Andrew Jeffers, Marco Palma, William E. Klingeman, Charles Hall, David Buckley, and Dean Kopsell

variables are pooled to become β9 “respondent demographics” and included in a modified conjoint preference model, the equation is expressed as: where V i = the error term. We used a two-limit Tobit model to account for the truncation residuals of the