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 |
| 0.275 | 0.605 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 12.07 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.000119 |
| Time: | 01:47:14 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.5323 | 42.073 | 1.890 | 0.074 | -8.527 167.591 |
| C(dose)[T.1] | 25.2387 | 78.054 | 0.323 | 0.750 | -138.129 188.607 |
| expression | -8.5617 | 14.071 | -0.608 | 0.550 | -38.012 20.889 |
| expression:C(dose)[T.1] | 9.6488 | 29.244 | 0.330 | 0.745 | -51.559 70.857 |
| Omnibus: | 0.098 | Durbin-Watson: | 1.927 |
| Prob(Omnibus): | 0.952 | Jarque-Bera (JB): | 0.262 |
| Skew: | 0.127 | Prob(JB): | 0.877 |
| Kurtosis: | 2.543 | Cond. No. | 64.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.89 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 2.47e-05 |
| Time: | 01:47:14 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.9252 | 36.168 | 2.016 | 0.057 | -2.519 148.369 |
| C(dose)[T.1] | 50.7702 | 9.989 | 5.082 | 0.000 | 29.933 71.608 |
| expression | -6.3279 | 12.057 | -0.525 | 0.605 | -31.478 18.823 |
| Omnibus: | 0.214 | Durbin-Watson: | 1.957 |
| Prob(Omnibus): | 0.899 | Jarque-Bera (JB): | 0.399 |
| Skew: | 0.153 | Prob(JB): | 0.819 |
| Kurtosis: | 2.432 | Cond. No. | 26.6 |
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: | 01:47:14 | 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: | 0.207 |
| Model: | OLS | Adj. R-squared: | 0.169 |
| Method: | Least Squares | F-statistic: | 5.472 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0293 |
| Time: | 01:47:14 | Log-Likelihood: | -110.44 |
| No. Observations: | 23 | AIC: | 224.9 |
| Df Residuals: | 21 | BIC: | 227.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 180.1299 | 43.403 | 4.150 | 0.000 | 89.868 270.392 |
| expression | -36.3311 | 15.531 | -2.339 | 0.029 | -68.629 -4.033 |
| Omnibus: | 0.903 | Durbin-Watson: | 2.178 |
| Prob(Omnibus): | 0.637 | Jarque-Bera (JB): | 0.866 |
| Skew: | 0.401 | Prob(JB): | 0.649 |
| Kurtosis: | 2.488 | Cond. No. | 21.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.133 | 0.721 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.308 |
| Method: | Least Squares | F-statistic: | 3.078 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0725 |
| Time: | 01:47:14 | Log-Likelihood: | -70.729 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.1147 | 64.836 | 0.680 | 0.510 | -98.588 186.817 |
| C(dose)[T.1] | 63.8798 | 90.590 | 0.705 | 0.495 | -135.507 263.267 |
| expression | 5.7323 | 15.670 | 0.366 | 0.721 | -28.756 40.221 |
| expression:C(dose)[T.1] | -3.7260 | 21.334 | -0.175 | 0.865 | -50.681 43.229 |
| Omnibus: | 2.283 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.319 | Jarque-Bera (JB): | 1.611 |
| Skew: | -0.772 | Prob(JB): | 0.447 |
| Kurtosis: | 2.558 | Cond. No. | 67.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.006 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0263 |
| Time: | 01:47:14 | Log-Likelihood: | -70.750 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 52.2902 | 43.011 | 1.216 | 0.247 | -41.422 146.002 |
| C(dose)[T.1] | 48.3230 | 15.835 | 3.052 | 0.010 | 13.822 82.824 |
| expression | 3.7221 | 10.195 | 0.365 | 0.721 | -18.491 25.935 |
| Omnibus: | 2.052 | Durbin-Watson: | 0.844 |
| Prob(Omnibus): | 0.358 | Jarque-Bera (JB): | 1.513 |
| Skew: | -0.732 | Prob(JB): | 0.469 |
| Kurtosis: | 2.471 | Cond. No. | 24.9 |
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: | 01:47:14 | 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: | 0.032 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.4260 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.525 |
| Time: | 01:47:14 | Log-Likelihood: | -75.058 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.3573 | 55.013 | 1.061 | 0.308 | -60.490 177.205 |
| expression | 8.4225 | 12.904 | 0.653 | 0.525 | -19.455 36.300 |
| Omnibus: | 0.462 | Durbin-Watson: | 1.687 |
| Prob(Omnibus): | 0.794 | Jarque-Bera (JB): | 0.548 |
| Skew: | 0.192 | Prob(JB): | 0.760 |
| Kurtosis: | 2.146 | Cond. No. | 24.7 |