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 |
| 0.242 | 0.628 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.15 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.000114 |
| Time: | 05:16:57 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.5809 | 157.348 | 0.309 | 0.761 | -280.752 377.914 |
| C(dose)[T.1] | 156.5527 | 213.469 | 0.733 | 0.472 | -290.244 603.349 |
| expression | 0.7790 | 21.764 | 0.036 | 0.972 | -44.774 46.332 |
| expression:C(dose)[T.1] | -13.8891 | 29.129 | -0.477 | 0.639 | -74.857 47.079 |
| Omnibus: | 0.117 | Durbin-Watson: | 1.844 |
| Prob(Omnibus): | 0.943 | Jarque-Bera (JB): | 0.325 |
| Skew: | 0.097 | Prob(JB): | 0.850 |
| Kurtosis: | 2.451 | Cond. No. | 478. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.84 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.51e-05 |
| Time: | 05:16:57 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.5932 | 102.640 | 1.019 | 0.320 | -109.509 318.696 |
| C(dose)[T.1] | 54.8687 | 9.257 | 5.927 | 0.000 | 35.559 74.178 |
| expression | -6.9745 | 14.183 | -0.492 | 0.628 | -36.560 22.611 |
| Omnibus: | 0.326 | Durbin-Watson: | 1.854 |
| Prob(Omnibus): | 0.849 | Jarque-Bera (JB): | 0.490 |
| Skew: | 0.090 | Prob(JB): | 0.783 |
| Kurtosis: | 2.308 | Cond. No. | 177. |
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: | 05:16:57 | 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: | 0.044 |
| Model: | OLS | Adj. R-squared: | -0.001 |
| Method: | Least Squares | F-statistic: | 0.9697 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.336 |
| Time: | 05:16:57 | Log-Likelihood: | -112.59 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -76.4758 | 158.770 | -0.482 | 0.635 | -406.656 253.704 |
| expression | 21.3112 | 21.641 | 0.985 | 0.336 | -23.694 66.317 |
| Omnibus: | 1.774 | Durbin-Watson: | 2.433 |
| Prob(Omnibus): | 0.412 | Jarque-Bera (JB): | 1.291 |
| Skew: | 0.356 | Prob(JB): | 0.525 |
| Kurtosis: | 2.084 | Cond. No. | 168. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.108 | 0.749 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 3.885 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0407 |
| Time: | 05:16:57 | Log-Likelihood: | -69.882 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 231.8111 | 166.536 | 1.392 | 0.191 | -134.733 598.355 |
| C(dose)[T.1] | -263.6374 | 266.708 | -0.988 | 0.344 | -850.657 323.383 |
| expression | -22.3232 | 22.564 | -0.989 | 0.344 | -71.986 27.340 |
| expression:C(dose)[T.1] | 42.6689 | 36.362 | 1.173 | 0.265 | -37.364 122.702 |
| Omnibus: | 2.588 | Durbin-Watson: | 1.177 |
| Prob(Omnibus): | 0.274 | Jarque-Bera (JB): | 1.470 |
| Skew: | -0.766 | Prob(JB): | 0.480 |
| Kurtosis: | 2.916 | Cond. No. | 327. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.363 |
| Method: | Least Squares | F-statistic: | 4.982 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0266 |
| Time: | 05:16:57 | Log-Likelihood: | -70.766 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.8259 | 132.820 | 0.834 | 0.420 | -178.565 400.217 |
| C(dose)[T.1] | 48.7997 | 15.716 | 3.105 | 0.009 | 14.557 83.042 |
| expression | -5.8934 | 17.970 | -0.328 | 0.749 | -45.047 33.260 |
| Omnibus: | 2.725 | Durbin-Watson: | 0.827 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.879 |
| Skew: | -0.845 | Prob(JB): | 0.391 |
| Kurtosis: | 2.617 | Cond. No. | 127. |
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: | 05:16:57 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.061 |
| Method: | Least Squares | F-statistic: | 0.1942 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.667 |
| Time: | 05:16:57 | Log-Likelihood: | -75.189 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 168.3207 | 169.697 | 0.992 | 0.339 | -198.287 534.929 |
| expression | -10.1877 | 23.117 | -0.441 | 0.667 | -60.129 39.753 |
| Omnibus: | 1.200 | Durbin-Watson: | 1.634 |
| Prob(Omnibus): | 0.549 | Jarque-Bera (JB): | 0.802 |
| Skew: | 0.169 | Prob(JB): | 0.670 |
| Kurtosis: | 1.919 | Cond. No. | 126. |