Table 16.
Logistic regression - worked example
| Candidate variables (database record) | Actual values | Predicted log odds | Overall predicted log odds |
|---|---|---|---|
| Age | 71 | Not-significant | 0 |
| Gender | Male | -0.23 | -0.23 |
| AC250 | 75 | 0.57 | 0.34 |
| AC500 | 70 | 0.72 | 1.06 |
| AC1000 | 80 | 2.08 | 3.14 |
| AC2000 | 90 | 1.19 | 4.33 |
| AC4000 | 100 | 0.40 | 4.73 |
| AC8000 | 100 | 0.09 | 4.82 |
| BC250 | 40 | -0.03 | 4.79 |
| BC500 | 60 | 0.56 | 5.35 |
| BC1000 | 65 | 0.56 | 5.91 |
| BC2000 | 70 | 0.14 | 6.05 |
| BC4000 | 70 | Not-significant | 6.05 |
| Diagnosis | Tinnitus | Not-significant | 6.05 |
| Hearing aid type | BTE | To be found | 6.05 |
| Masker | No masker | Not-significant | 6.05 |
| Mould | 2107 | 4.09 | 10.14 |
| Free-text words | REV | -0.16+0.12 = -0.04 | 10.1 |