Phoenix - Your AI AssistantPoisson or Negative Binomial Regression — Count Data Analysis
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What you get with this Offer
I will conduct a count data regression analysis in R or Python — choosing between Poisson regression, Negative Binomial regression, Zero-Inflated Poisson (ZIP), or Zero-Inflated Negative Binomial (ZINB) based on overdispersion and zero-inflation testing — with incidence rate ratio reporting, model fit comparison, and a results report suitable for epidemiological, ecological, or social science publication. Using linear regression on count data violates the normality assumption and produces predicted negative counts; choosing the wrong count model (Poisson when overdispersion is present, for example) produces standard errors and inference that are statistically incorrect.
The analysis covers overdispersion testing (Cameron-Trivedi), zero-inflation testing (Vuong test), model selection between Poisson, NB, ZIP, and ZINB (AIC comparison), incidence rate ratio table with 95% CIs, offset variable incorporation for rate modelling, and a plain-language results interpretation.
Designed for epidemiologists, ecologists, health researchers, criminologists, and any analyst modelling count outcomes (hospital admissions, species counts, incident frequencies, etc.).
The analysis covers overdispersion testing (Cameron-Trivedi), zero-inflation testing (Vuong test), model selection between Poisson, NB, ZIP, and ZINB (AIC comparison), incidence rate ratio table with 95% CIs, offset variable incorporation for rate modelling, and a plain-language results interpretation.
Designed for epidemiologists, ecologists, health researchers, criminologists, and any analyst modelling count outcomes (hospital admissions, species counts, incident frequencies, etc.).
What the Freelancer needs to start the work
Please share your dataset, your count outcome variable, your predictor list, any offset variable for rate modelling, your statistical software (R or Python preferred), your research question, and your output format.