# HealthCare

I am needing to use Rstudio and/or Excel to answer these questions.

Please include either your R-Script or Excel file used to answer these questions. We're using the same dataset as the M4 HW. Here is the datasetPreview the document and here is the codebookPreview the document.

1. Create groupings for education and age. For education, classify each individual into one of the following groups: no degree, GED/diploma,bachelor's degree,Masters or doctorate,all others. For age, classify each individual as 18-29,30-39,40-49,50-59,60-69, or 70+. You may do this by creating a series of binary/dummy variables (0 or 1; definitely do this if you are using Excel) or creating one categorical/factor variable to use in the regression. If you're using binary variables, don't create a dummy for the 18-29 year old and no degree groups. If you're using a factor variable, make sure to specify this in the regression in R.

2. Run a linear regression to predict the annual out of pocket expenditures for an individual using the following [login to view URL] constant term will automatically pop up in the regression. You don't need to add it to the regression or Script.

Out of pocket expenditures=Constant+age+education+has private insurance+expected total payments

a. Are all four of the independent variables significant?

b. Predict the out of pocket expenditures for a 42 year-old individual with a bachelor's degree, private health insurance, and annual total payments of \$10,000.

c. What are two more variables in the dataset that you think would be related to out of pocket expenditures? Why?

d. Add the variables to the regression and provide the results. Were your predictions correct?

3. For each age group defined in question 4, determine the mean number of medications prescribed, percentage of individuals ever diagnosed with cancer (exclude all individuals who are NIU from this percentage), percentage of individuals in very good or excellent health (again excluding all individuals who are NIU), and median total income. Then, create a table to visualize these results. Note: Even though I plan on using R for the assignment, I will likely construct the table in Excel.

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