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 |
| 0.208 | 0.653 | 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): | 8.04e-05 |
| Time: | 09:39:33 | 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 | 150.5665 | 108.692 | 1.385 | 0.182 | -76.928 378.061 |
| C(dose)[T.1] | -176.2486 | 231.803 | -0.760 | 0.456 | -661.417 308.920 |
| expression | -10.5033 | 11.829 | -0.888 | 0.386 | -35.263 14.256 |
| expression:C(dose)[T.1] | 24.2398 | 24.190 | 1.002 | 0.329 | -26.390 74.869 |
| Omnibus: | 0.506 | Durbin-Watson: | 2.059 |
| Prob(Omnibus): | 0.776 | Jarque-Bera (JB): | 0.597 |
| Skew: | -0.129 | Prob(JB): | 0.742 |
| Kurtosis: | 2.255 | Cond. No. | 599. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.79 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.56e-05 |
| Time: | 09:39:33 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.3848 | 94.864 | 1.027 | 0.317 | -100.498 295.268 |
| C(dose)[T.1] | 55.8066 | 10.268 | 5.435 | 0.000 | 34.387 77.226 |
| expression | -4.7063 | 10.320 | -0.456 | 0.653 | -26.232 16.820 |
| Omnibus: | 0.631 | Durbin-Watson: | 1.994 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.635 |
| Skew: | 0.010 | Prob(JB): | 0.728 |
| Kurtosis: | 2.186 | Cond. No. | 208. |
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: | 09:39:33 | 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: | 0.140 |
| Model: | OLS | Adj. R-squared: | 0.099 |
| Method: | Least Squares | F-statistic: | 3.410 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0789 |
| Time: | 09:39:33 | Log-Likelihood: | -111.37 |
| No. Observations: | 23 | AIC: | 226.7 |
| Df Residuals: | 21 | BIC: | 229.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -154.6706 | 127.101 | -1.217 | 0.237 | -418.992 109.651 |
| expression | 24.8686 | 13.467 | 1.847 | 0.079 | -3.137 52.874 |
| Omnibus: | 2.529 | Durbin-Watson: | 2.158 |
| Prob(Omnibus): | 0.282 | Jarque-Bera (JB): | 1.224 |
| Skew: | 0.123 | Prob(JB): | 0.542 |
| Kurtosis: | 1.897 | Cond. No. | 181. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.597 | 0.133 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.439 |
| Method: | Least Squares | F-statistic: | 4.658 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0246 |
| Time: | 09:39:33 | Log-Likelihood: | -69.151 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 212.4522 | 223.075 | 0.952 | 0.361 | -278.532 703.437 |
| C(dose)[T.1] | 202.5734 | 296.434 | 0.683 | 0.509 | -449.873 855.020 |
| expression | -19.4090 | 29.820 | -0.651 | 0.528 | -85.043 46.225 |
| expression:C(dose)[T.1] | -22.8980 | 40.661 | -0.563 | 0.585 | -112.392 66.596 |
| Omnibus: | 1.136 | Durbin-Watson: | 0.617 |
| Prob(Omnibus): | 0.567 | Jarque-Bera (JB): | 0.981 |
| Skew: | -0.479 | Prob(JB): | 0.612 |
| Kurtosis: | 2.193 | Cond. No. | 404. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.547 |
| Model: | OLS | Adj. R-squared: | 0.471 |
| Method: | Least Squares | F-statistic: | 7.240 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00866 |
| Time: | 09:39:33 | Log-Likelihood: | -69.364 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 12 | BIC: | 146.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 304.4774 | 147.468 | 2.065 | 0.061 | -16.827 625.782 |
| C(dose)[T.1] | 35.9113 | 16.481 | 2.179 | 0.050 | 0.002 71.820 |
| expression | -31.7250 | 19.687 | -1.611 | 0.133 | -74.619 11.169 |
| Omnibus: | 2.019 | Durbin-Watson: | 0.681 |
| Prob(Omnibus): | 0.364 | Jarque-Bera (JB): | 1.338 |
| Skew: | -0.500 | Prob(JB): | 0.512 |
| Kurtosis: | 1.931 | Cond. No. | 154. |
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: | 09:39:33 | 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: | 0.368 |
| Model: | OLS | Adj. R-squared: | 0.319 |
| Method: | Least Squares | F-statistic: | 7.555 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0166 |
| Time: | 09:39:33 | Log-Likelihood: | -71.864 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 13 | BIC: | 149.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 479.1654 | 140.484 | 3.411 | 0.005 | 175.668 782.663 |
| expression | -53.1821 | 19.349 | -2.749 | 0.017 | -94.982 -11.382 |
| Omnibus: | 0.146 | Durbin-Watson: | 1.413 |
| Prob(Omnibus): | 0.929 | Jarque-Bera (JB): | 0.357 |
| Skew: | 0.092 | Prob(JB): | 0.836 |
| Kurtosis: | 2.267 | Cond. No. | 129. |