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Public Health – Statistical Analysis

STATISTICS PROJECT
STATISTICS PROJECT (SCENARIO BASED)
July 5, 2019

Public Health – Statistical Analysis

Public Health

 

 

Table of Content

Introduction. 3

Methodology. 3

Data Analysis. 3

ARIMA.. 4

Regression. 8

Sensitivity Analysis. 12

Conclusion. 13

References. 14

Introduction

This is a statistical report which is carried out for the purpose of analysing the impact of polio and whooping cough vaccination programs over the macroeconomic indicators in Columbia and Mexico for the time period of 1950 to 1990. This objective is tested out for the purpose of improving the macroeconomic indicators specifically for the countries Mexico and Columbia in terms of morbidity and mortality.

Methodology

In order to fulfil the objective of the research, there are different statistical tests have been applied. To run the tests in an efficient manner, software of STATA have been used. There are three tests which are specifically used: ARIMA, logistic regression model, and sensitivity analysis. For the purpose of analysing the data it is important to differentiate among the independent variables and dependent variables of the research. For the data set which is under consideration for this research, the dependent variables are PIB per capita, human development index, and life expectancy. On the other hand the independent variables of the research are morbidity, mortality, demographics, vaccination coverage, and whooping cough.

 

Data Analysis

ARIMA

ARIMA is an important acronym for Autoregressive Integrated Moving Average which is used for the purpose of analysing the time series data in order to predict the future points (Baum, 2004). Firstly, ARIMA has been carried out for Mexico and then for Columbia in order to analyse the aforementioned independent and dependent variables with respect to countries and diseaseas (Polio and Whooping Cough).

The above table shows ARIMA which is carried out for Mexico. This model basically determines whether each term in the model is significant or not which can be predicted by the sig value which should be lower than 0.05. Based on the significance value it can be said that only demographics is significant in the model with respect to PIB per capita because its probability value is 0.033.

The above table shows ARIMA which is carried out for Mexico with respect to life expectancy. Based on the significance value it can be said that morbidity and mortality is significant in the model with respect to life expectancy because their probability values are 0.001 and 0.000 respectively.

The above table shows ARIMA which is carried out for Mexico with respect to Human Development Index. Based on the significance value it can be said that polio and demographics are significant in the model with respect to human development index because their probability values are 0.000 and 0.000 respectively.

ARIMA have been carried out for Mexico with respect to Whooping cough as well, the following section include the results by taking whooping cough as one of the independent variables:

The above table shows ARIMA which is carried out for Mexico with respect to PIB per capita. This model basically determines whether each term in the model is significant or not which can be predicted by the sig value which should be lower than 0.05. Based on the significance value it can be said that morbidity and whooping cough are significant in the model with respect to PIB per capita because their probability values are 0.000 and 0.003 respectively.

The above table shows ARIMA which is carried out for Mexico with respect to life expectancy. Based on the significance value it can be said that mortality and demographics are significant in the model with respect to life expectancy because their probability values are 0.000 and 0.000 respectively.

The above table shows ARIMA which is carried out for Mexico with respect to human development index. Based on the significance value it can be said that mortality, morbidity, and demographics are significant in the model with respect to human development index because their probability values are 0.000, 0.000 and 0.000 respectively.

The next section shows the analysis of ARIMA which have been carried out for country two that is Columbia for the two diseases which are into consideration: polio and whooping cough.

The above table shows ARIMA which is carried out for Columbia with respect to PIB per capita for the disease polio. Based on the significance value it can be said that only demographics is significant in the model with respect to PIB per capita because its probability value is 0.000.

The above table shows ARIMA which is carried out for Columbia with respect to life expectancy for the disease polio. Based on the significance value it can be said that only demographics is significant in the model with respect to life expectancy because their probability value is 0.000.

The above table shows ARIMA which is carried out for Columbia with respect to Human Development Index for polio. Based on the significance value it can be said that mortality, polio, and demographics are significant in the model with respect to human development index because their probability values are 0.000, 0.000 and 0.000 respectively.

The above table shows ARIMA which is carried out for Columbia with respect to PIB per capita for the disease whooping cough. This model basically determines whether each term in the model is significant or not which can be predicted by the sig value which should be lower than 0.05. Based on the significance value it can be said that only demographics is significant in the model with respect to PIB per capita because its probability value is 0.000.

The above table shows ARIMA which is carried out for Columbia with respect to life expectancy for the disease whooping. This model basically determines whether each term in the model is significant or not which can be predicted by the sig value which should be lower than 0.05. Based on the significance value it can be said that only demographics is significant in the model with respect to life expectancy because their probability value is 0.000.

The above table shows ARIMA which is carried out for Columbia with respect to Human Development Index for whooping cough. This model basically determines whether each term in the model is significant or not which can be predicted by the sig value which should be lower than 0.05. Based on the significance value it can be said that mortality, polio, and demographics are significant in the model with respect to human development index because their probability values are 0.000, 0.000 and 0.000 respectively.

Regression

Regression is carried out for the purpose of finding out the impact of independent variable on the dependent variables of the research (Hamilton, 2012). Following section includes the regression analysis which has been carried out separately for Mexico and Columbia for polio and whooping cough.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of PIB per capita on morbidity, mortality, polio, and demographics. For the purpose of signifying the relationship among the independent variables and dependent variable of the research the p value should be less than 0.05. The value of R-square determines that the independent variables are able to determine 94.03% of the changes caused in PIB per capita. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and PIB per capita for Mexico.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of life expectancy on morbidity, mortality, polio, and demographics. The value of R-square determines that the independent variables are able to determine 98.34% of the changes caused in life expectancy. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and life expectancy for Mexico.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of human development on morbidity, mortality, polio, and demographics. The value of R-square determines that the independent variables are able to determine 98.84% of the changes caused in human development index. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and human development index for Mexico.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of PIB per capita on morbidity, mortality, whooping cough, and demographics. For the purpose of signifying the relationship among the independent variables and dependent variable of the research the p value should be less than 0.05. The value of R-square determines that the independent variables are able to determine 91.37% of the changes caused in PIB per capita. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and PIB per capita for Mexico.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of life expectancy on morbidity, mortality, whooping, and demographics. The value of R-square determines that the independent variables are able to determine 99.59% of the changes caused in life expectancy. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and life expectancy for Mexico.

In the above table the results of regression analysis are shown for Mexico in order to determine the impact of human development on morbidity, mortality, whooping cough, and demographics. The value of R-square determines that the independent variables are able to determine 99.39% of the changes caused in human development index. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and human development index for Mexico.

The following section shows the results of regression for the second country that is Columbia for the diseases which are taken into consideration: polio and whooping cough.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of PIB per capita on morbidity, mortality, polio, and demographics. The value of R-square determines that the independent variables are able to determine 98.54% of the changes caused in PIB per capita. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and PIB per capita for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of life expectancy on morbidity, mortality, polio, and demographics. The value of R-square determines that the independent variables are able to determine 95.37% of the changes caused in life expectancy. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and life expectancy for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of human development on morbidity, mortality, polio, and demographics. The value of R-square determines that the independent variables are able to determine 99.71% of the changes caused in human development index. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and human development index for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of PIB per capita on morbidity, mortality, whooping cough, and demographics. The value of R-square determines that the independent variables are able to determine 98.46% of the changes caused in PIB per capita. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and PIB per capita for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of life expectancy on morbidity, mortality, whooping, and demographics. The value of R-square determines that the independent variables are able to determine 95.34% of the changes caused in life expectancy. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and life expectancy for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of human development on morbidity, mortality, whooping cough, and demographics. The value of R-square determines that the independent variables are able to determine 99.72% of the changes caused in human development index. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and human development index for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of PIB per capita on morbidity, mortality, whooping cough, and demographics. The value of R-square determines that the independent variables are able to determine 98.46% of the changes caused in PIB per capita. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and PIB per capita for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of life expectancy on morbidity, mortality, whooping, and demographics. The value of R-square determines that the independent variables are able to determine 95.34% of the changes caused in life expectancy. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and life expectancy for Columbia.

In the above table the results of regression analysis are shown for Columbia in order to determine the impact of human development on morbidity, mortality, whooping cough, and demographics. The value of R-square determines that the independent variables are able to determine 99.72% of the changes caused in human development index. The sig value for the model is also less than 0.05 hence there is a significant relationship among independent variables and human development index for Columbia.

Sensitivity Analysis

It is basically carried out for the purpose of assessing the impact of values of independent variables on the values of a dependent variable. Sensitivity analysis for this data set is carried out for the purpose of assessing polio and whooping cough vaccination programs on the macroeconomic indicators in Columbia and Mexico. Following are the results

Conclusion

To sum up, the selected statistical tests have yielded significant results which show the impact of independent variables on dependent variables of the research. Moreover, it has been identified with the help of the statistical results that different vaccination programs for polio and whooping cough in Mexico and Columbia can help improvement in macroeconomic indicators.

References

Baum, C.F., 2004. Stata: The language of choice for time series analysis?. Working Papers in Economics, p.4.

Hamilton, L.C., 2012. Statistics with Stata: version 12. Cengage Learning.

 

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