AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.218 | 0.283 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 12.86 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 8.05e-05 |
| Time: | 09:20:24 | Log-Likelihood: | -100.35 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.2155 | 49.590 | 0.186 | 0.855 | -94.577 113.009 |
| C(dose)[T.1] | 69.5148 | 68.705 | 1.012 | 0.324 | -74.286 213.316 |
| expression | 8.6367 | 9.448 | 0.914 | 0.372 | -11.139 28.412 |
| expression:C(dose)[T.1] | -2.9392 | 13.271 | -0.221 | 0.827 | -30.716 24.838 |
| Omnibus: | 0.164 | Durbin-Watson: | 1.950 |
| Prob(Omnibus): | 0.921 | Jarque-Bera (JB): | 0.371 |
| Skew: | 0.099 | Prob(JB): | 0.830 |
| Kurtosis: | 2.410 | Cond. No. | 110. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.23 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.57e-05 |
| Time: | 09:20:24 | Log-Likelihood: | -100.38 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.9767 | 34.243 | 0.496 | 0.625 | -54.453 88.406 |
| C(dose)[T.1] | 54.4234 | 8.571 | 6.350 | 0.000 | 36.544 72.302 |
| expression | 7.1469 | 6.475 | 1.104 | 0.283 | -6.360 20.654 |
| Omnibus: | 0.234 | Durbin-Watson: | 1.970 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.413 |
| Skew: | 0.165 | Prob(JB): | 0.814 |
| Kurtosis: | 2.433 | Cond. No. | 43.4 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 38.84 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 09:20:24 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 21 | BIC: | 208.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
| C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.060 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04951 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.826 |
| Time: | 09:20:24 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.2571 | 56.462 | 1.191 | 0.247 | -50.161 184.675 |
| expression | 2.4257 | 10.902 | 0.223 | 0.826 | -20.245 25.097 |
| Omnibus: | 2.913 | Durbin-Watson: | 2.487 |
| Prob(Omnibus): | 0.233 | Jarque-Bera (JB): | 1.508 |
| Skew: | 0.302 | Prob(JB): | 0.470 |
| Kurtosis: | 1.900 | Cond. No. | 42.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.573 | 0.464 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 3.938 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0392 |
| Time: | 09:20:24 | Log-Likelihood: | -69.829 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.8422 | 65.727 | 2.021 | 0.068 | -11.822 277.506 |
| C(dose)[T.1] | -174.4582 | 222.608 | -0.784 | 0.450 | -664.416 315.499 |
| expression | -10.7711 | 10.664 | -1.010 | 0.334 | -34.241 12.699 |
| expression:C(dose)[T.1] | 37.5702 | 37.523 | 1.001 | 0.338 | -45.016 120.157 |
| Omnibus: | 3.525 | Durbin-Watson: | 1.176 |
| Prob(Omnibus): | 0.172 | Jarque-Bera (JB): | 1.724 |
| Skew: | -0.817 | Prob(JB): | 0.422 |
| Kurtosis: | 3.300 | Cond. No. | 208. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.404 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0212 |
| Time: | 09:20:24 | Log-Likelihood: | -70.483 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.4145 | 63.104 | 1.813 | 0.095 | -23.078 251.907 |
| C(dose)[T.1] | 47.8938 | 15.473 | 3.095 | 0.009 | 14.181 81.607 |
| expression | -7.7368 | 10.225 | -0.757 | 0.464 | -30.015 14.542 |
| Omnibus: | 2.286 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.319 | Jarque-Bera (JB): | 1.662 |
| Skew: | -0.775 | Prob(JB): | 0.436 |
| Kurtosis: | 2.494 | Cond. No. | 51.2 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 10.58 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 09:20:24 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 13 | BIC: | 147.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
| C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
| Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.054 |
| Model: | OLS | Adj. R-squared: | -0.019 |
| Method: | Least Squares | F-statistic: | 0.7394 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.405 |
| Time: | 09:20:24 | Log-Likelihood: | -74.885 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 161.0274 | 78.956 | 2.039 | 0.062 | -9.548 331.602 |
| expression | -11.2582 | 13.092 | -0.860 | 0.405 | -39.543 17.026 |
| Omnibus: | 2.763 | Durbin-Watson: | 1.699 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.212 |
| Skew: | 0.268 | Prob(JB): | 0.545 |
| Kurtosis: | 1.715 | Cond. No. | 49.5 |