# Automobile Insurance Claims

### Description

Claims experience from a large midwestern (US) property and casualty insurer for private passenger automobile insurance. The dependent variable is the amount paid on a closed claim, in (US) dollars (claims that were not closed by year end are handled separately). Insurers categorize policyholders according to a risk classification system. This insurer's risk classification system is based on automobile operator characteristics and vehicle characteristics, and these factors are summarized by the risk class categorical variable CLASS.

### Usage

1 |

### Format

A data frame with 6773 observations on the following 5 variables.

`STATE`

Codes 01 to 17 used, with each code randomly assigned to an actual individual state, a factor with levels

`STATE 01`

`STATE 02`

`STATE 03`

`STATE 04`

`STATE 06`

`STATE 07`

`STATE 10`

`STATE 11`

`STATE 12`

`STATE 13`

`STATE 14`

`STATE 15`

`STATE 17`

`CLASS`

Rating class of operator, based on age, gender, marital status, use of vehicle, a factor with levels

`C1`

`C11`

`C1A`

`C1B`

`C1C`

`C2`

`C6`

`C7`

`C71`

`C72`

`C7A`

`C7B`

`C7C`

`F1`

`F11`

`F6`

`F7`

`F71`

`GENDER`

a factor with levels

`F`

`M`

`AGE`

Age of operator, a numeric vector

`PAID`

Amount paid to settle and close a claim, a numeric vector

### Details

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/

DataDescriptions.pdf

### Source

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/data.html

### References

Frees E.W. (2010), Regression Modeling with Actuarial and Financial Applications, Cambridge University Press.

### Examples

1 2 | ```
data(AutoClaims)
## maybe str(AutoClaims) ; plot(AutoClaims) ...
``` |