Classification Rule: yi = 1 if P(yi = 1) > 0.5, otherwise yi = 0
| Observation | X (Regressor) | Y (Binary Outcome) | Predicted Probability | yi |
|---|
Probit Regression with Contingency Table
Raw Data
Contingency Table
| yi = 0 | yi = 1 | Total | |
|---|---|---|---|
| P(yi = 1) ≤ 0.5 or yi = 0 | 0 | 0 | 0 |
| P(yi = 1) > 0.5 or yi = 1 | 0 | 0 | 0 |
| Total | 0 | 0 | 20 |
Model Performance
Accuracy: 0.00%
Estimated Probit Model
P(yi = 1|X) = Φ(0.000 + 0.000X)