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 |
| 0.546 | 0.468 | 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: | 12.88 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 7.97e-05 |
| Time: | 23:57:03 | Log-Likelihood: | -100.34 |
| 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 | 11.0019 | 39.469 | 0.279 | 0.783 | -71.609 93.612 |
| C(dose)[T.1] | 99.7362 | 57.661 | 1.730 | 0.100 | -20.950 220.422 |
| expression | 11.4243 | 10.314 | 1.108 | 0.282 | -10.163 33.011 |
| expression:C(dose)[T.1] | -12.2252 | 14.664 | -0.834 | 0.415 | -42.918 18.468 |
| Omnibus: | 0.108 | Durbin-Watson: | 2.298 |
| Prob(Omnibus): | 0.947 | Jarque-Bera (JB): | 0.332 |
| Skew: | -0.022 | Prob(JB): | 0.847 |
| Kurtosis: | 2.413 | Cond. No. | 71.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.27 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 2.16e-05 |
| Time: | 23:57:03 | Log-Likelihood: | -100.75 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.8724 | 28.159 | 1.203 | 0.243 | -24.867 92.612 |
| C(dose)[T.1] | 52.2357 | 8.780 | 5.949 | 0.000 | 33.921 70.550 |
| expression | 5.3771 | 7.276 | 0.739 | 0.468 | -9.800 20.554 |
| Omnibus: | 0.980 | Durbin-Watson: | 2.110 |
| Prob(Omnibus): | 0.613 | Jarque-Bera (JB): | 0.803 |
| Skew: | 0.135 | Prob(JB): | 0.669 |
| Kurtosis: | 2.125 | Cond. No. | 27.4 |
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: | Thu, 30 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:57:03 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | 0.009 |
| Method: | Least Squares | F-statistic: | 1.194 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.287 |
| Time: | 23:57:03 | Log-Likelihood: | -112.47 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.3461 | 45.725 | 0.664 | 0.514 | -64.745 125.437 |
| expression | 12.7248 | 11.645 | 1.093 | 0.287 | -11.493 36.943 |
| Omnibus: | 2.490 | Durbin-Watson: | 2.589 |
| Prob(Omnibus): | 0.288 | Jarque-Bera (JB): | 1.659 |
| Skew: | 0.439 | Prob(JB): | 0.436 |
| Kurtosis: | 2.021 | Cond. No. | 27.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.146 | 0.709 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.308 |
| Method: | Least Squares | F-statistic: | 3.080 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.0723 |
| Time: | 23:57:03 | Log-Likelihood: | -70.726 |
| 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 | 38.9443 | 76.056 | 0.512 | 0.619 | -128.453 206.342 |
| C(dose)[T.1] | 66.9592 | 119.267 | 0.561 | 0.586 | -195.546 329.465 |
| expression | 9.0777 | 23.939 | 0.379 | 0.712 | -43.611 61.766 |
| expression:C(dose)[T.1] | -5.7395 | 37.138 | -0.155 | 0.880 | -87.479 76.000 |
| Omnibus: | 2.298 | Durbin-Watson: | 0.892 |
| Prob(Omnibus): | 0.317 | Jarque-Bera (JB): | 1.626 |
| Skew: | -0.775 | Prob(JB): | 0.443 |
| Kurtosis: | 2.552 | Cond. No. | 65.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.017 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.0261 |
| Time: | 23:57:03 | Log-Likelihood: | -70.743 |
| 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 | 46.4273 | 56.216 | 0.826 | 0.425 | -76.057 168.911 |
| C(dose)[T.1] | 48.7015 | 15.699 | 3.102 | 0.009 | 14.497 82.906 |
| expression | 6.6929 | 17.542 | 0.382 | 0.709 | -31.527 44.913 |
| Omnibus: | 2.220 | Durbin-Watson: | 0.870 |
| Prob(Omnibus): | 0.329 | Jarque-Bera (JB): | 1.613 |
| Skew: | -0.763 | Prob(JB): | 0.446 |
| Kurtosis: | 2.498 | Cond. No. | 25.6 |
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: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:57:03 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2463 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.628 |
| Time: | 23:57:03 | Log-Likelihood: | -75.159 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.1152 | 72.340 | 0.803 | 0.436 | -98.166 214.397 |
| expression | 11.1893 | 22.547 | 0.496 | 0.628 | -37.519 59.898 |
| Omnibus: | 0.907 | Durbin-Watson: | 1.768 |
| Prob(Omnibus): | 0.635 | Jarque-Bera (JB): | 0.702 |
| Skew: | 0.133 | Prob(JB): | 0.704 |
| Kurtosis: | 1.975 | Cond. No. | 25.3 |