CASE STUDY Healthcare: Spending and Outcomes It goes without saying that healthcare is an essential element

CASE STUDY

Healthcare: Spending and Outcomes

It goes without saying that healthcare is an essential element of any society. The Organisation for Economic Co-operation and Development (OECD) publishes annual data on healthcare, among other important topics. OECD's origins date back to 1960 , but its roots are directly post World War II. Currently, there are 34 member countries, but OECD also works closely with emerging giants like China, India, and Brazil and developing economies in Africa, Asia, Latin America, and the Caribbean.

In many ways, the United States is a "healthcare outlier." For instance, in 2011, the per capita health expenditure in the United States was $\$ 8508$, over two and two-thirds times that of the average of the other 33 member countries. In addition, as a percentage of gross domestic product (GDP), total healthcare expenditures in the United States were , almost twice that of the average of the others. Moreover, the OECD reported that the United States ranks relatively poorly among those countries on measures such as life expectancy and infant mortality.

The following table provides selected OECD healthcare data for the 34 member countries for the year 2011 or, in a few cases, the nearest year to that. Note that % GDP is an abbreviation for "healthcare expenditures as a percentage of gross domestic product," LE for "life expectancy" (at birth), and IMR for "infant mortality rate" (number of deaths of babies under 1 year of age per 1000 live births).

Recall that %GDP is an abbreviation for "healthcare expenditures as a percentage of gross domestic product," LE for "life expectancy" (at birth), and IMR for "infant mortality rate" (number of deaths of babies under 1 year of age per 1000 live births). Now that you have studied regression and correlation, you can analyze the relationship between %GDP and LE, or the relationship between %GDP and IMR. I recommend that you use Microsoft Excel or another piece of technology to solve the following problems.

Choose either LE or IMR to analyze, and answer the following questions accordingly.

Considering % GDP as the predictor (independent) variable and LE or IMR as the response (dependent) variable, construct a scatterplot of the data.
Does the data point for the US appear to be an outlier? Explain your answer and discuss it in the context of %GDP and either LE or IMR.
Find the regression equation for the data. Plot the regression lines and the data points.
Remove the data point for the US, and find the regression equation for the new data. Plot the regression lines and the data points.
Is the data point for the US an influential observation? Explain your answer and discuss it in the context of %GDP and LE or IMR.
Obtain the coefficient of determination (R ² ), both with and without the data point for the US. Interpret your results.
Obtain correlation coefficient (r), both with and without the data point for the US. Interpret your results.
This week, we learned about linear regressions. Are there any applications from this week’s content to your major, your profession, or your interests? Please explain.