Medical Insurance Charges (Answers)

1 Overview

This notebook uses a real healthcare-related dataset on medical insurance charges.

You will be tasked to conduct analysis on the medical insurance charges.

1.1 Your Task

1. Import and load a dataset in R.
2. Identify outcome and predictor variables.
3. Fit simple and multiple linear regression models & carry out forward selection based on p-values.
4. Interpret coefficients, p-values, and model fit statistics.
5. Make predictions from a fitted regression model.

2 Load the data

2.1 Question 1:

Go to the following URL (https://www.kaggle.com/datasets/mirichoi0218/insurance/data)

Download the data in csv and print out the first few rows of the data.

Click on this link for answers to question 1

check that you have the data set as shown below.

df <- read.csv("~/Documents/Projects/Regression Practice/insurance.csv")
head(df)

  age    sex    bmi children smoker    region   charges
1  19 female 27.900        0    yes southwest 16884.924
2  18   male 33.770        1     no southeast  1725.552
3  28   male 33.000        3     no southeast  4449.462
4  33   male 22.705        0     no northwest 21984.471
5  32   male 28.880        0     no northwest  3866.855
6  31 female 25.740        0     no southeast  3756.622

3 Data preparation

3.1 Question 2a:

List the variables that are categorical?

Ans: There are three variables that are categorical. sex, smoker and region.

3.2 Question 2b:

What should you do with the data type of these variables before fitting a regression model?

Ans: These data are currently in data type characters. We would have to make them into a factor before we can do any modelling.

df$sex <- as.factor(df$sex)
df$smoker <- as.factor(df$smoker)
df$region <- as.factor(df$region)

str(df)

'data.frame':   1338 obs. of  7 variables:
 $ age     : int  19 18 28 33 32 31 46 37 37 60 ...
 $ sex     : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 2 1 ...
 $ bmi     : num  27.9 33.8 33 22.7 28.9 ...
 $ children: int  0 1 3 0 0 0 1 3 2 0 ...
 $ smoker  : Factor w/ 2 levels "no","yes": 2 1 1 1 1 1 1 1 1 1 ...
 $ region  : Factor w/ 4 levels "northeast","northwest",..: 4 3 3 2 2 3 3 2 1 2 ...
 $ charges : num  16885 1726 4449 21984 3867 ...

summary(df)

      age            sex           bmi           children     smoker    
 Min.   :18.00   female:662   Min.   :15.96   Min.   :0.000   no :1064  
 1st Qu.:27.00   male  :676   1st Qu.:26.30   1st Qu.:0.000   yes: 274  
 Median :39.00                Median :30.40   Median :1.000             
 Mean   :39.21                Mean   :30.66   Mean   :1.095             
 3rd Qu.:51.00                3rd Qu.:34.69   3rd Qu.:2.000             
 Max.   :64.00                Max.   :53.13   Max.   :5.000             
       region       charges     
 northeast:324   Min.   : 1122  
 northwest:325   1st Qu.: 4740  
 southeast:364   Median : 9382  
 southwest:325   Mean   :13270  
                 3rd Qu.:16640  
                 Max.   :63770  

3.3 Question 2c:

Which is the response variable?

Ans: Based on the question in overview, our response variable should be charges.