TABLE 1.
The parameters of the linear regression model fit to the data in Figure 5 (on a log-log scale, see Data analysis).
| Linear regression model | |||||
|---|---|---|---|---|---|
| 0.3 < f probe < 16 kHz | 3 < f probe < 16 kHz | ||||
| CAP-STC slope | 0.27 | (p < 0.001) | 0.35 | (p < 0.001) | |
| Slope difference | SF-GD vs CAP-STC | 0.11 | (p = 0.066) | – | n.s. |
| SF-STC vs CAP-STC | 0.59 | (p < 0.001) | – | n.s. | |
| CAP-STC intercept | −0.40 | (p = 0.007) | −0.74 | (p = 0.009) | |
| Intercept difference | SF-GD vs CAP-STC | −0.33 | (p = 0.12) | 0.11 | (p = 0.001) |
| SF-STC vs CAP-STC | – | – | −0.13 | (p < 0.001) | |
The model was fit to data over either the full range of probe frequencies or to data obtained only for high frequencies (>3 kHz). Both models were significant (p < 0.001) and explained 82.7 and 44.0 % of variability in sharpness of tuning, respectively (based on the adjusted R 2). In the first “full” model, most of the variance was predicted by the probe frequency (R 2 = 0.42) while in the other “restricted” model by the variables coding the test type (R 2 = 0.26). The intercept estimate for SF-STC in the first model was omitted due to significant change in slope (i.e. significant interaction between test type and frequency).