In the following report, the variable Maternal Mortality, and the variable Number of Physicians will be examined. The hypothesis going into this examination is that the two variables will have a strong relationship. The aim of this bivariate investigation is to determine whether or not this theory is correct.
At first examination, it was decided that the two variables would be related, as the likelihood of maternal death during childbirth is increasingly unlikely when the number of doctors present is higher. With this knowledge, it is predicted that the two variables listed above will have a strong correlation. After looking over data from both variables, Number of Physicians was deemed the independent variable, while Maternal Mortality became …show more content…
It was necessary to use a regression table that consisted of these transformed variables because the relationship was not otherwise linear. For a unit increase in the square-rooted Number of Physicians, the logged Maternal Mortality decreases by 2.05. While this association appears weak, the fact that the variables have been transformed must be considered. By looking at the aforementioned Pearson’s R, as well as beta, which is the standardized regression coefficient and had a value of -0.7528 (refer to Table 5), it is revealed that is a very strong negative association. While this was a satisfactory revelation, it was decided to investigate the impact of an alternative variable on Maternal Mortality. A dichotomous variable for location was generated; individuals either lived in Africa or did not live in Africa (refer to Table 6). When the location of Africa was controlled in regards to the relationship between Physician Number and Maternal Mortality, it was discovered that location influences these variables (refer to Table 7). For those who lived in Africa, with each unit increase in the square-rooted Number of Physicians, the log of Maternal Mortality decreases by 1.757; however, it is important to note that the African coefficient has a positive correlation to maternal mortality, meaning that in relation to …show more content…
The Number of Physicians does affect the Maternal Mortality Rate, in that there is a strong negative correlation. The significance of this association is that as the Number of Physicians increases, Maternal Mortality rates will decrease. However, after factoring in location, in this case Africa, it was noted that location does indeed play a role in the Number of Physicians in relation to Maternal Mortality. The role that location plays could be considered spurious, in that it could reduce the likelihood of physicians present. It could also influence Maternal Mortality in that it is more likely for women in developing nations to die during childbirth. Moreover, location could also play an antecedent role in that African locations have fewer doctors, which in turn results in higher maternal deaths. In the future, it would be beneficial to compare each continent to one another regarding these variables; for example, how South America may compare to Africa in regards to maternal death rates, as both contain developing regions. Other things to consider in future research with these variables include but are not limited to: mother’s occupation, household income, and number of
At first examination, it was decided that the two variables would be related, as the likelihood of maternal death during childbirth is increasingly unlikely when the number of doctors present is higher. With this knowledge, it is predicted that the two variables listed above will have a strong correlation. After looking over data from both variables, Number of Physicians was deemed the independent variable, while Maternal Mortality became …show more content…
It was necessary to use a regression table that consisted of these transformed variables because the relationship was not otherwise linear. For a unit increase in the square-rooted Number of Physicians, the logged Maternal Mortality decreases by 2.05. While this association appears weak, the fact that the variables have been transformed must be considered. By looking at the aforementioned Pearson’s R, as well as beta, which is the standardized regression coefficient and had a value of -0.7528 (refer to Table 5), it is revealed that is a very strong negative association. While this was a satisfactory revelation, it was decided to investigate the impact of an alternative variable on Maternal Mortality. A dichotomous variable for location was generated; individuals either lived in Africa or did not live in Africa (refer to Table 6). When the location of Africa was controlled in regards to the relationship between Physician Number and Maternal Mortality, it was discovered that location influences these variables (refer to Table 7). For those who lived in Africa, with each unit increase in the square-rooted Number of Physicians, the log of Maternal Mortality decreases by 1.757; however, it is important to note that the African coefficient has a positive correlation to maternal mortality, meaning that in relation to …show more content…
The Number of Physicians does affect the Maternal Mortality Rate, in that there is a strong negative correlation. The significance of this association is that as the Number of Physicians increases, Maternal Mortality rates will decrease. However, after factoring in location, in this case Africa, it was noted that location does indeed play a role in the Number of Physicians in relation to Maternal Mortality. The role that location plays could be considered spurious, in that it could reduce the likelihood of physicians present. It could also influence Maternal Mortality in that it is more likely for women in developing nations to die during childbirth. Moreover, location could also play an antecedent role in that African locations have fewer doctors, which in turn results in higher maternal deaths. In the future, it would be beneficial to compare each continent to one another regarding these variables; for example, how South America may compare to Africa in regards to maternal death rates, as both contain developing regions. Other things to consider in future research with these variables include but are not limited to: mother’s occupation, household income, and number of