Build a regression model predicting insurance charges

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Reference no: EM133288784

Complete this exercise either using either SAS or SPSS Only

Healthcare providers like Humana rely heavily on providing Medicare & Medicaid Services. Centers for Medicare & Medicaid Services (CMS) monitor provider performance. Providers like Humana have to walk a very fine line because on one side they want to minimize their costs, while on the other, they want to minimize readmission rates.

The readmission rate is a very important metric in CMS' performance evaluation of the provider. A readmission rate is defined as a rate at which a provider has its patients readmitted into the hospital within 30 days of being discharged. Readmissions are costly as CMS penalizes healthcare providers if their members readmit at a higher rate because CMS thinks proper care was not provided the first time around resulting in readmission. Therefore, providers cannot afford to provide less care to save money.

A healthcare company identified that individual healthcare cost could be reduced by focusing on preventative care of high-risk patients. The company identified three critical factors that they believe would influence insurance charges thereby help identify high risk individuals:

Age: Age of a member. Older the member, higher would be their healthcare cost.
BMI: Body Mass Index of a member. Higher the member BMI, higher would be their healthcare cost.
Children: Number of children. Higher the number of children, more exercise a member gets, lower is their healthcare cost.
Your objectives are as follows:

Question 1: Build a regression model predicting insurance charges. Identify the most important variables contributing to cost burden. Predict insurance charges (dependent variable) of each member using member data (Age, BMI, Children; independent variables).

Question 2: Comment on the fit of the model. Specifically, does the regression model you built show a good fit? Why/why not?

Question 3: Explain how much variance is being explained by the regression model. Why is this number not 100%? What are other factors that you think could be missing?

Question 4: What is the interpretation of the coefficients for Age, BMI, Children? Explain in detail, if any of the independent variables have a relationship counter to what the healthcare company expected.

Reference no: EM133288784

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