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 |
| 0.448 | 0.511 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000104 |
| Time: | 22:55:50 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -346.6841 | 493.713 | -0.702 | 0.491 | -1380.037 686.669 |
| C(dose)[T.1] | 419.9393 | 777.620 | 0.540 | 0.595 | -1207.638 2047.516 |
| expression | 33.4636 | 41.208 | 0.812 | 0.427 | -52.787 119.714 |
| expression:C(dose)[T.1] | -30.6914 | 63.695 | -0.482 | 0.635 | -164.006 102.623 |
| Omnibus: | 0.123 | Durbin-Watson: | 1.808 |
| Prob(Omnibus): | 0.940 | Jarque-Bera (JB): | 0.327 |
| Skew: | 0.106 | Prob(JB): | 0.849 |
| Kurtosis: | 2.456 | Cond. No. | 2.70e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.13 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.27e-05 |
| Time: | 22:55:50 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -192.7851 | 369.188 | -0.522 | 0.607 | -962.898 577.328 |
| C(dose)[T.1] | 45.3133 | 14.800 | 3.062 | 0.006 | 14.442 76.185 |
| expression | 20.6172 | 30.813 | 0.669 | 0.511 | -43.658 84.892 |
| Omnibus: | 0.172 | Durbin-Watson: | 1.887 |
| Prob(Omnibus): | 0.918 | Jarque-Bera (JB): | 0.382 |
| Skew: | 0.079 | Prob(JB): | 0.826 |
| Kurtosis: | 2.389 | Cond. No. | 1.05e+03 |
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: | 22:55:50 | 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: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.472 |
| Method: | Least Squares | F-statistic: | 20.65 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000177 |
| Time: | 22:55:50 | Log-Likelihood: | -105.23 |
| No. Observations: | 23 | AIC: | 214.5 |
| Df Residuals: | 21 | BIC: | 216.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1101.1345 | 259.883 | -4.237 | 0.000 | -1641.591 -560.678 |
| expression | 97.0611 | 21.357 | 4.545 | 0.000 | 52.646 141.476 |
| Omnibus: | 1.276 | Durbin-Watson: | 2.027 |
| Prob(Omnibus): | 0.528 | Jarque-Bera (JB): | 0.942 |
| Skew: | 0.192 | Prob(JB): | 0.624 |
| Kurtosis: | 2.086 | Cond. No. | 621. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.076 | 0.788 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.328 |
| Method: | Least Squares | F-statistic: | 3.278 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0625 |
| Time: | 22:55:50 | Log-Likelihood: | -70.510 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -97.8990 | 470.874 | -0.208 | 0.839 | -1134.287 938.489 |
| C(dose)[T.1] | 432.8980 | 595.201 | 0.727 | 0.482 | -877.131 1742.927 |
| expression | 16.2905 | 46.383 | 0.351 | 0.732 | -85.798 118.379 |
| expression:C(dose)[T.1] | -37.2233 | 58.036 | -0.641 | 0.534 | -164.959 90.513 |
| Omnibus: | 4.325 | Durbin-Watson: | 0.945 |
| Prob(Omnibus): | 0.115 | Jarque-Bera (JB): | 2.363 |
| Skew: | -0.963 | Prob(JB): | 0.307 |
| Kurtosis: | 3.260 | Cond. No. | 1.09e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.954 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0270 |
| Time: | 22:55:50 | Log-Likelihood: | -70.786 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.3979 | 276.140 | 0.519 | 0.613 | -458.260 745.056 |
| C(dose)[T.1] | 51.3184 | 17.481 | 2.936 | 0.012 | 13.231 89.405 |
| expression | -7.4856 | 27.186 | -0.275 | 0.788 | -66.719 51.748 |
| Omnibus: | 3.189 | Durbin-Watson: | 0.736 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 2.026 |
| Skew: | -0.895 | Prob(JB): | 0.363 |
| Kurtosis: | 2.809 | Cond. No. | 368. |
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: | 22:55:50 | 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: | 0.059 |
| Model: | OLS | Adj. R-squared: | -0.014 |
| Method: | Least Squares | F-statistic: | 0.8125 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.384 |
| Time: | 22:55:50 | Log-Likelihood: | -74.845 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -191.6369 | 316.678 | -0.605 | 0.555 | -875.777 492.503 |
| expression | 27.6997 | 30.731 | 0.901 | 0.384 | -38.690 94.090 |
| Omnibus: | 1.277 | Durbin-Watson: | 1.638 |
| Prob(Omnibus): | 0.528 | Jarque-Bera (JB): | 0.832 |
| Skew: | 0.188 | Prob(JB): | 0.660 |
| Kurtosis: | 1.909 | Cond. No. | 334. |