<abstract xmlns="http://www.w3.org/1999/xhtml">

This research fitted a discrete distribution for modeling count data. Specifically, Zero-Inflated Poisson (ZIP) regression was used to model plant protein consumption by 400 randomly sampled individuals in Wukari. The data was collected by questionnaire. The ZIP regression model was used based on its ability to model data with excess zeros present in the collected data. Variables considered and used for the analysis are Age, Body Mass Index, Blood Pressure, Occupation, Gender, Weight, Height, Body Reaction, and Consumption Class. The parameters of the ZIP model were estimated using the maximum likelihood estimation technique. The model was tested for Goodness of Fit (GoF) using deviance, scaled deviance, Pearson–<italic>χ</italic>2 and scaled Pearson–<italic>χ</italic>2 statistics. The results obtained showed that Age, Gender, and Reaction were significant at 5%, and the GoF tests revealed that the Zero-Inflated Poisson regression produces a good fit and is a good model for overcoming the overdispersion effect.
</abstract>

This research fitted a discrete distribution for modeling count data. Specifically, Zero-Inflated Poisson (ZIP) regression was used to model plant protein consumption by 400 randomly sampled individuals in Wukari. The data was collected by questionnaire. The ZIP regression model was used based on its ability to model data with excess zeros present in the collected data. Variables considered and used for the analysis are Age, Body Mass Index, Blood Pressure, Occupation, Gender, Weight, Height, Body Reaction, and Consumption Class. The parameters of the ZIP model were estimated using the maximum likelihood estimation technique. The model was tested for Goodness of Fit (GoF) using deviance, scaled deviance, Pearson–χ2 and scaled Pearson–χ2 statistics. The results obtained showed that Age, Gender, and Reaction were significant at 5%, and the GoF tests revealed that the Zero-Inflated Poisson regression produces a good fit and is a good model for overcoming the overdispersion effect.

Zero-Inflated Poisson Regression Modeling of Plant Protein Consumption

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Zero-Inflated Poisson Regression Modeling of Plant Protein Consumption"}

This research fitted a discrete distribution for modeling count data. Specifically, Zero-Inflated Poisson (ZIP) regression was used to model plant protein...

Zero-Inflated Poisson Regression Modeling of Plant Protein Consumption

Publié en ligne: 29 déc. 2023

Pages: 149 - 157

DOI: https://doi.org/10.2478/bile-2023-0010

Mots clés
Zero-inflated, Poisson regression model, Protein, Maximum likelihood estimation, Goodness of fit

© 2023 I. J. David et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Zero-Inflated Poisson Regression Modeling of Plant Protein Consumption

Publié en ligne: 29 déc. 2023

Pages: 149 - 157

DOI: https://doi.org/10.2478/bile-2023-0010

Mots clésZero-inflated, Poisson regression model, Protein, Maximum likelihood estimation, Goodness of fit

© 2023 I. J. David et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Zero-inflated, Poisson regression model, Protein, Maximum likelihood estimation, Goodness of fit