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Examples
Insurance Fraud
127 insurance claims were examined by a team of adjusters and judged to be either fraudulent or legitimate. Of interest is the relationship between fraud and 3 broad groups of covariates: Accident (AC1, AC9, AC 16), Claimant (CL7, CL11) and Injury (IJ2, IJ3, IJ4, IJ6, IJ12).
A “1” indicates the claim had that particular characteristic, a “0” indicates the claim did not have that particular characteristic. (The data are available in LogXact .cyl format and ASCII .dat format.)We thank the Automobile Insurance Bureau of Massachusetts for permission to use these data.Challenge: Try fitting a logistic regression model to the data with all ten covariates included.
Solution | Download LogXact Data | Download ASCII Data
Fraudulent Insurance Claims and their Relationship to Covariates of Interest
| Fraud/Total |
(%fraud) |
AC1 |
AC9 |
AC16 |
CL7 |
CL11 |
IJ2 |
IJ3 |
IJ4 |
IJ6 |
IJ12 |
| 0/22 |
0% |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| 0/4 |
0% |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
| 0/2 |
0% |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 0/3 |
0% |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 0/10 |
0% |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
| 0/2 |
0% |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
| 0/4 |
0% |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
| 1/1 |
100% |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
| 1/4 |
25% |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
| 1/1 |
100% |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
| 0/1 |
0% |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
1 |
0 |
| 0/8 |
0% |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
| 0/3 |
0% |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
0 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
| 1/1 |
100% |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
1 |
0 |
0 |
| 0/1 |
0% |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
| 1/1 |
100% |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
| 1/1 |
100% |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
| 0/1 |
0% |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 1/1 |
100% |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
| 1/1 |
100% |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
| 0/10 |
0% |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 0/2 |
0% |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| 0/8 |
0% |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
| 1/7 |
14% |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| 1/6 |
17% |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
| 0/3 |
0% |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
0 |
| 0/3 |
0% |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
| 0/2 |
0% |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
| 0/1 |
0% |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
0 |
| 1/1 |
100% |
1 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
| 1/1 |
100% |
1 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
1 |
| 0/1 |
0% |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 1/1 |
100% |
1 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
Example of how to read this table: The last line tells us that there was one fraudulent (and no legitimate -- i.e. one total) claim with two accident characteristics (AC1 and AC9) and two injury characteristics (IJ2 and IJ6).
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