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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

desirable attribute of a product is expected to be always positive (i.e., greater than zero), the use of a censored model such as the Tobit model was appropriate. The model specification for estimating the WTP for Texas Superstar™-labeled produce and Earth

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

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

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

are desirable. Using a Tobit model, we jointly modeled these two outcomes. Of the logs with nonzero production, ailanthus averaged 0.055 kg/log on 2 of 37 logs, red maple 0.188 kg/log on 13 of 37 logs, sweetgum 0.123 kg/log on 27 of 32 logs, and white

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

purchasing at mass merchandiser/home improvement centers and nursery/greenhouse garden centers, we used a two-limit tobit model developed by Rossett and Nelson (1975) . A pollinator-friendly plant purchasing value was obtained by asking survey respondents

-limit tobit model for estimating part-worth values to avoid the biased parameter problem of ordinary linear squares (OLS). Using OLS would yield truncated residuals and asymptotically biased estimates, and therefore, a two-limit tobit model was constructed

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