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 |
| 0.785 | 0.386 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.49 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.97e-05 |
| Time: | 22:45:55 | Log-Likelihood: | -99.984 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.0749 | 145.449 | 0.021 | 0.983 | -301.353 307.503 |
| C(dose)[T.1] | 240.0749 | 178.126 | 1.348 | 0.194 | -132.746 612.896 |
| expression | 6.9785 | 19.834 | 0.352 | 0.729 | -34.534 48.491 |
| expression:C(dose)[T.1] | -25.0543 | 24.095 | -1.040 | 0.311 | -75.486 25.377 |
| Omnibus: | 2.064 | Durbin-Watson: | 1.995 |
| Prob(Omnibus): | 0.356 | Jarque-Bera (JB): | 1.476 |
| Skew: | 0.412 | Prob(JB): | 0.478 |
| Kurtosis: | 2.072 | Cond. No. | 441. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.61 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.93e-05 |
| Time: | 22:45:55 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 127.4643 | 82.902 | 1.538 | 0.140 | -45.466 300.394 |
| C(dose)[T.1] | 55.0836 | 8.826 | 6.241 | 0.000 | 36.674 73.494 |
| expression | -9.9977 | 11.285 | -0.886 | 0.386 | -33.538 13.542 |
| Omnibus: | 0.570 | Durbin-Watson: | 2.018 |
| Prob(Omnibus): | 0.752 | Jarque-Bera (JB): | 0.663 |
| Skew: | 0.253 | Prob(JB): | 0.718 |
| Kurtosis: | 2.340 | Cond. No. | 146. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:45:55 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.09681 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.759 |
| Time: | 22:45:56 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.2207 | 136.774 | 0.272 | 0.788 | -247.216 321.657 |
| expression | 5.7344 | 18.430 | 0.311 | 0.759 | -32.593 44.062 |
| Omnibus: | 3.628 | Durbin-Watson: | 2.464 |
| Prob(Omnibus): | 0.163 | Jarque-Bera (JB): | 1.681 |
| Skew: | 0.318 | Prob(JB): | 0.432 |
| Kurtosis: | 1.838 | Cond. No. | 144. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.810 | 0.203 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.526 |
| Model: | OLS | Adj. R-squared: | 0.396 |
| Method: | Least Squares | F-statistic: | 4.062 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0361 |
| Time: | 22:45:56 | Log-Likelihood: | -69.708 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -65.2953 | 227.458 | -0.287 | 0.779 | -565.927 435.336 |
| C(dose)[T.1] | -46.5690 | 296.863 | -0.157 | 0.878 | -699.960 606.822 |
| expression | 16.7904 | 28.740 | 0.584 | 0.571 | -46.467 80.047 |
| expression:C(dose)[T.1] | 12.1722 | 37.536 | 0.324 | 0.752 | -70.444 94.789 |
| Omnibus: | 2.043 | Durbin-Watson: | 0.858 |
| Prob(Omnibus): | 0.360 | Jarque-Bera (JB): | 1.454 |
| Skew: | -0.575 | Prob(JB): | 0.483 |
| Kurtosis: | 1.999 | Cond. No. | 433. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.521 |
| Model: | OLS | Adj. R-squared: | 0.441 |
| Method: | Least Squares | F-statistic: | 6.527 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0121 |
| Time: | 22:45:56 | Log-Likelihood: | -69.779 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -121.7037 | 140.986 | -0.863 | 0.405 | -428.887 185.479 |
| C(dose)[T.1] | 49.5708 | 14.675 | 3.378 | 0.005 | 17.598 81.544 |
| expression | 23.9264 | 17.784 | 1.345 | 0.203 | -14.822 62.674 |
| Omnibus: | 2.315 | Durbin-Watson: | 0.975 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.555 |
| Skew: | -0.586 | Prob(JB): | 0.460 |
| Kurtosis: | 1.944 | Cond. No. | 155. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:45:56 | 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: | 0.066 |
| Model: | OLS | Adj. R-squared: | -0.006 |
| Method: | Least Squares | F-statistic: | 0.9120 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.357 |
| Time: | 22:45:56 | Log-Likelihood: | -74.792 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -86.2697 | 188.673 | -0.457 | 0.655 | -493.873 321.333 |
| expression | 22.7871 | 23.861 | 0.955 | 0.357 | -28.762 74.336 |
| Omnibus: | 0.418 | Durbin-Watson: | 1.752 |
| Prob(Omnibus): | 0.812 | Jarque-Bera (JB): | 0.515 |
| Skew: | 0.120 | Prob(JB): | 0.773 |
| Kurtosis: | 2.125 | Cond. No. | 154. |