Bayesian Logistic Regression Analysis of Dental Caries - Simeneh, Mekuanint
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Dental caries is a significant public health problem. It is a disease with multi-factorial causes. Dental caries is an infectious, transmissible bacterial disease; the most pre - dominant bacterial species are streptococcus mutans and bacilli species, as in ordinary pattern of dental caries. The main objective of this study is to determine statistical association between dental caries and some risk factors using Bayesian and classical logistic regression method. The study is based on data from the March 2009 through March 2013 dental caries patients Hawassa Haik Poly Higher Clinic. The…mehr

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Produktbeschreibung
Dental caries is a significant public health problem. It is a disease with multi-factorial causes. Dental caries is an infectious, transmissible bacterial disease; the most pre - dominant bacterial species are streptococcus mutans and bacilli species, as in ordinary pattern of dental caries. The main objective of this study is to determine statistical association between dental caries and some risk factors using Bayesian and classical logistic regression method. The study is based on data from the March 2009 through March 2013 dental caries patients Hawassa Haik Poly Higher Clinic. The explanatory variables: age, sex, place of residence, region and habit of cleaning teeth were found to have a significant effect of dental caries patients. Cleaning teeth of patients was found to be statistical significant in different levels of dental caries. The Bayesian approach has given estimates of the parameters with smaller standard errors showing that it is more accurate than classical approach.
Autorenporträt
The author of this book was born in Amhara Region in 1988 G.C. When his age reached for education,he joined Yenssa and high school education at Tana haik. He joined Gondar University in 2008 G.C and graduated in 2011 G.C with BSc degree in Statistics. He joined Hawassa University in 2011 G.c graduated in 2013 G.C with M.Sc.in statistical modeling.