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searching for Binomial regression 8 found (77 total)

Joseph Hilbe (2,740 words) [view diff] case mismatch in snippet view article find links to article

regression. Among his most influential books are two editions of Negative Binomial Regression (Cambridge University Press, 2007, 2011), Modeling Count Data (Cambridge

Poisson distribution (11,215 words) [view diff] exact match in snippet view article find links to article

|D|)^{k}e^{-\lambda |D|}}{k!}}.} Poisson regression and negative binomial regression are useful for analyses where the dependent (response) variable is

Negative binomial distribution (8,233 words) [view diff] exact match in snippet view article find links to article

}}k=0,1,2,\dotsc } Here r is a real, positive number. In negative binomial regression, the distribution is specified in terms of its mean, m = r ( 1 −

Generalized functional linear model (2,869 words) [view diff] case mismatch in snippet view article find links to article

Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included, are

Length of stay (702 words) [view diff] exact match in snippet view article find links to article

researchers. Carter & Potts (2014) instead recommend use of negative binomial regression. Length of stay is commonly used as a quality metric. The prospective

GLIM (software) (606 words) [view diff] exact match in snippet view article

University Press. ISBN 0-19-852203-7. Wacholder, Sholom (1986). "Binomial regression in GLIM: Estimating risk ratios and risk differences". American Journal

Fred C. Nelles Youth Correctional Facility (1,893 words) [view diff] exact match in snippet view article find links to article

Justice in August 2002. Using both survival models and negative binomial regression models, the results indicate that there were no significant differences

Bias in the introduction of variation (14,511 words) [view diff] exact match in snippet view article find links to article

parameter β {\displaystyle \beta } , defined as a coefficient of binomial regression of observed counts on the expected counts from a mutational model