Introduction
Medical insurance is usually charged based on the health factors as well as the number
of dependencies of the beneficiary. Body mass index (bmi) is an important health index.
In this assignment, you will answer the following questions:
• How does bmi depend on the residential region of the beneficiaries.
• How do insurance charges depend on the beneficiary’s smoking habit and their
number of children.
Data Description
Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. The dataset is used as an example for regression in the book. The
data is downloaded from https://www.kaggle.com/mirichoi0218/insurance. Some
post-processing was carried out for the purpose of the assignment.
The data file for this assignment is called insurance.sas7bdat. If you are using SAS
University edition, the file can be downloaded in the assessment page. This is the same
data as in Assignment 1. The dataset contains insurance charges to the beneficiary,
together with their demographic information. Variables in that file are as follows:
Variable Description
Age Age of the primary beneficiary
sex insurance contractor gender (female/ male)
bmi Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m) using the ratio of height to weight, ideally 18.5 to 24.9
weight_range Weight classification according to bmi ( underweigh(bmi < 8.5) / healthy (18.5 ≤ bmi < 25) / overweigh(25 ≤ bmi < 30)/ obese(bmi ≥ 30))
children Number of children covered by health insurance / Number of dependents
smoker Smoking (yes/no)
region the beneficiary’s residential area in the US, northeast, southeast, southwest, northwest
charges Individual medical costs billed by health insurance
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