Document Type:Case Study

Subject Area:Economics

Document 1

This study has used infant mortality rate as the response variable. Therefore, the study focused on factors that lead to high infant mortality rate and low birth weights in the United States. The data used for this case study accurately represents a biological data collected from the United States. The dataset comprised of variables that were used to study the implications of high mortality rate and low birth weight in the United States. The data in this case study is modelled through multiple regression models that explain the impact of explanatory variables to the response variable. From this model, one can conclude which explanatory variables had a significant impact on the measured variable. Background The high infant mortality rate in the US is attributed to many economic, environmental and behavioural factors. There is a huge disparity between the infant mortality rates for different races in the US. This study addresses issues associated with these disparities between the infant mortality rate and body weight loss for different races in the US. According to David (2016), these infant deaths may occur due to some toxic emissions from the environment surrounding us. These toxic environments expose the majority of the residents to great risk and hence high infant death rates. Infant mortality commission chaired by David (2005), reviewed the growing gap between the black and white infant deaths rates as majorly a medical problem in the US. The above commission also focused on the economic, environmental and behavioural conditions that serve as major causes of the high infant mortality rates.

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This case study, therefore, focused on how factors such as the race of a certain community could influence the level of infant mortality rate and the body weight loss of a newborn. It also explores the effect of body weight at birth, families that live above or below the poverty level, body mass index (BMI), and insurance coverage of mothers and affordability of care for a period of 12 months. Analysis This section shows the results of the findings of the study. It discusses all the relevant information that is required in the interpretation of the results. Tables and graphs have been used to elaborate the whole idea of the topic of study. This study project used multiple linear regression to explain the relationship between the independent variables and the dependent variable.

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Results of the regression model were further used to test the significance of various variables that were present in the multiple regression models. Also using the results from the regression results we could judge the adequacy of the model using the R-squared value. The R-squared value was 0. This indicated the model could only account for 3. of its variation. Thus this was not a good model. X3-200. X4 Now, using the above equation we can predict body weight of a newborn at any given time. For instance, if we want to determine the body weight of a given newborn given the BMI of the mother and keeping all other factors constant it will be very possible. We just pick the BMI of the mother and substitute in the above regression equation.

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In the case of a categorical variable, we need to form a dummy variable and then generate our reference /base variable. which is a poor measure indicating malnutrition of the mother. The maximum value of the BMI was 50. which also not a good value because it’s too high and considered to be toxic to the body of the mother. Body weight was also another continuous variable in this case study. The findings of the study reviewed that the mean body weight was 3289. Conclusion and Recommendations In conclusion, high body weight and not extremely high is a vital requirement for a good health. Therefore good measures should be undertaken to improve the health of many suffering individuals. For instance, mothers should be fed with good foods and undergo some medical check-ups in order to standardize their BMI level.

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We also need to take precautions on toxic environmental components that may expose us to toxic elements. This would even reduce the infant mortality rates. Total 3870 1. E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95. Upper 95. Intercept 3173. Poverty level -59. Standard Error 0. Median 3346 Median 26. Mode 3176 Mode 26. Standard Deviation 615. Standard Deviation 5. Count 3871 Count 3871 Largest(1) 5485 Largest(1) 50. Smallest(1) 500 Smallest(1) 16. The table 3, above shows the descriptive statistics of the continuous variables used in this case study. The mean of body weight (grams) was 3289. and the mean of the BMI of the mother was 27. Danaei, G. Lin, J. K. Paciorek, C. J. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.  Jama, 307(5), 491-497. Kochanek, K. D. Miniño, A. M. van Baak, M. Jebb, S. A. Papadaki, A.

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E. Infant mortality statistics from the 2013 period linked birth/infant death data set. Murphy, S. L. Xu, J. M. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010.  Jama, 307(5), 483-490.

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