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Identification of Best Fit Probability Distribution for Infant Birth Weight

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  • Ilesanmi A.O

    (Department Of Statistics, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria.)

  • Odukoya E.A

    (Department Of Statistics, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria.)

  • Aladejana A.E

    (Department Of Statistics, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria.)

  • Ogunboyo Ojo Femi

    (Epidemiology And Biostatistics Department, University of Medical Sciences Ondo, Ondo State, Nigeria)

  • Ajewole K.P

    (Department Of Physical Sciences, Mathematics Program, Landmark University, Omu- Aran.)

  • Popoola O.E.

    (Mathematical Sciences Department, Bamidele Olumilua University of Education, Science and Technology, Ikere.)

Abstract

Birth weight is the first weight recorded for a newborn, taken within 0 to 30minutesofdelivery. It is an indicator of a newborn’s chances for survival and growth. This study identified the best fit probability distribution for newborn birth weight in Ekiti State Nigeria. In the year 2021 there were 349 births recorded, with 163 males and 186 females. Also, in 2022 the number of births increased slightly to 353, comprising 179 males and 174 females. The selection of the best fit probability distribution was determined by Goodness of fit such as AIC, BIC, CAIC. Birth weights recorded within 0 to 30 minutes of delivery were obtained from Federal Teaching Hospital Ido-Ekiti and it comprise of both male and female newborn babies between 2021 and 2022.The analysis reveals that the mean birth weight is consistently higher for males compared to females in both 2021 and 2022. The results also showed that Weibull distribution is the least suitable model, with the highest AIC (1073.675), BIC (1077.800), and CAIC (1075.887) values, indicating a poor fit for the birth weight data follow by Normal and Gumbel distributions while the Log-normal distribution has the lowest AIC (737.110), BIC (741.234) and CAIC (739.321). This indicates that Log-Normal distribution is the most appropriate among the four probability distributions considered in this study.

Suggested Citation

  • Ilesanmi A.O & Odukoya E.A & Aladejana A.E & Ogunboyo Ojo Femi & Ajewole K.P & Popoola O.E., 2024. "Identification of Best Fit Probability Distribution for Infant Birth Weight," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 9(8), pages 590-597, August.
    • Handle: RePEc:bjf:journl:v:9:y:2024:i:8:p:590-597
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