ABSTRACT
Several methods which have been adopted to analyze multi-category data yields unsatisfactory results because of strict assumptions regarding normality, linearity, and homoscedasticity. As a result, Multinomial logistic regression is considered as an alternative because it does not assume normality, linearity, or homoscedasticity (Hosmer & Lemeshow, (2000)). The study attempted to use Maximum likelihood estimation and predicted probability to model Maternal Health Care Services data based on a set of explanatory variables. Also to determine the indices that affect Mortality rate. The result shows that wealth index has a significant impact on the use of public and private health delivery facilities. Educational level, antenatal care, assistance during delivery and place of residence are also important factors in assessing Maternal Health Care Services. Finally, the study revealed that educated women, who are wealthy, living in urban areas and who received antenatal care services and assistance during delivery are more likely to utilize Maternal Health Care Services (MHCS)
A, A (2021). Modelling Of Maternal Health Care Services Using Multinomial Logistic Regression. Afribary. Retrieved from https://afribary.com/works/modelling-of-maternal-health-care-services-using-multinomial-logistic-regression
A, Adewara "Modelling Of Maternal Health Care Services Using Multinomial Logistic Regression" Afribary. Afribary, 01 May. 2021, https://afribary.com/works/modelling-of-maternal-health-care-services-using-multinomial-logistic-regression. Accessed 20 May. 2024.
A, Adewara . "Modelling Of Maternal Health Care Services Using Multinomial Logistic Regression". Afribary, Afribary, 01 May. 2021. Web. 20 May. 2024. < https://afribary.com/works/modelling-of-maternal-health-care-services-using-multinomial-logistic-regression >.
A, Adewara . "Modelling Of Maternal Health Care Services Using Multinomial Logistic Regression" Afribary (2021). Accessed May 20, 2024. https://afribary.com/works/modelling-of-maternal-health-care-services-using-multinomial-logistic-regression