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 |
| 0.403 | 0.533 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.699 |
| Model: | OLS | Adj. R-squared: | 0.651 |
| Method: | Least Squares | F-statistic: | 14.68 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.47e-05 |
| Time: | 11:44:15 | Log-Likelihood: | -99.311 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 19 | BIC: | 211.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.5728 | 41.013 | 2.672 | 0.015 | 23.731 195.415 |
| C(dose)[T.1] | -78.9378 | 79.902 | -0.988 | 0.336 | -246.174 88.299 |
| expression | -8.1756 | 5.996 | -1.363 | 0.189 | -20.726 4.375 |
| expression:C(dose)[T.1] | 20.6136 | 12.569 | 1.640 | 0.117 | -5.694 46.921 |
| Omnibus: | 1.207 | Durbin-Watson: | 1.622 |
| Prob(Omnibus): | 0.547 | Jarque-Bera (JB): | 1.014 |
| Skew: | 0.300 | Prob(JB): | 0.602 |
| Kurtosis: | 2.164 | Cond. No. | 149. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.07 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 2.32e-05 |
| Time: | 11:44:15 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.8035 | 37.646 | 2.067 | 0.052 | -0.726 156.333 |
| C(dose)[T.1] | 51.2870 | 9.264 | 5.536 | 0.000 | 31.963 70.611 |
| expression | -3.4843 | 5.488 | -0.635 | 0.533 | -14.932 7.964 |
| Omnibus: | 0.004 | Durbin-Watson: | 1.862 |
| Prob(Omnibus): | 0.998 | Jarque-Bera (JB): | 0.165 |
| Skew: | -0.026 | Prob(JB): | 0.921 |
| Kurtosis: | 2.588 | Cond. No. | 58.5 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:44:15 | 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: | 0.129 |
| Model: | OLS | Adj. R-squared: | 0.087 |
| Method: | Least Squares | F-statistic: | 3.104 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0926 |
| Time: | 11:44:15 | Log-Likelihood: | -111.52 |
| No. Observations: | 23 | AIC: | 227.0 |
| Df Residuals: | 21 | BIC: | 229.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 171.0726 | 52.285 | 3.272 | 0.004 | 62.339 279.806 |
| expression | -14.0752 | 7.989 | -1.762 | 0.093 | -30.688 2.538 |
| Omnibus: | 2.294 | Durbin-Watson: | 2.234 |
| Prob(Omnibus): | 0.318 | Jarque-Bera (JB): | 1.538 |
| Skew: | 0.407 | Prob(JB): | 0.464 |
| Kurtosis: | 2.029 | Cond. No. | 52.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.177 | 0.100 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.573 |
| Model: | OLS | Adj. R-squared: | 0.457 |
| Method: | Least Squares | F-statistic: | 4.925 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0208 |
| Time: | 11:44:15 | Log-Likelihood: | -68.914 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 11 | BIC: | 148.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 182.9844 | 205.693 | 0.890 | 0.393 | -269.742 635.711 |
| C(dose)[T.1] | 168.6905 | 248.027 | 0.680 | 0.510 | -377.214 714.595 |
| expression | -20.5719 | 36.570 | -0.563 | 0.585 | -101.062 59.919 |
| expression:C(dose)[T.1] | -21.2976 | 44.088 | -0.483 | 0.639 | -118.335 75.740 |
| Omnibus: | 0.895 | Durbin-Watson: | 0.806 |
| Prob(Omnibus): | 0.639 | Jarque-Bera (JB): | 0.692 |
| Skew: | -0.471 | Prob(JB): | 0.708 |
| Kurtosis: | 2.531 | Cond. No. | 288. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.564 |
| Model: | OLS | Adj. R-squared: | 0.492 |
| Method: | Least Squares | F-statistic: | 7.767 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00685 |
| Time: | 11:44:15 | Log-Likelihood: | -69.071 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 12 | BIC: | 146.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 265.2958 | 111.479 | 2.380 | 0.035 | 22.403 508.189 |
| C(dose)[T.1] | 49.0799 | 13.996 | 3.507 | 0.004 | 18.586 79.574 |
| expression | -35.2254 | 19.763 | -1.782 | 0.100 | -78.284 7.834 |
| Omnibus: | 0.883 | Durbin-Watson: | 0.718 |
| Prob(Omnibus): | 0.643 | Jarque-Bera (JB): | 0.757 |
| Skew: | -0.468 | Prob(JB): | 0.685 |
| Kurtosis: | 2.423 | Cond. No. | 93.1 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 11:44:15 | 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: | 0.118 |
| Model: | OLS | Adj. R-squared: | 0.050 |
| Method: | Least Squares | F-statistic: | 1.731 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.211 |
| Time: | 11:44:15 | Log-Likelihood: | -74.362 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 293.2892 | 152.015 | 1.929 | 0.076 | -35.120 621.698 |
| expression | -35.5490 | 27.018 | -1.316 | 0.211 | -93.917 22.819 |
| Omnibus: | 0.790 | Durbin-Watson: | 1.951 |
| Prob(Omnibus): | 0.674 | Jarque-Bera (JB): | 0.650 |
| Skew: | 0.078 | Prob(JB): | 0.722 |
| Kurtosis: | 1.992 | Cond. No. | 92.4 |