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 |
| 0.906 | 0.353 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.739 |
| Model: | OLS | Adj. R-squared: | 0.698 |
| Method: | Least Squares | F-statistic: | 17.93 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.07e-06 |
| Time: | 22:50:01 | Log-Likelihood: | -97.657 |
| No. Observations: | 23 | AIC: | 203.3 |
| Df Residuals: | 19 | BIC: | 207.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 353.2471 | 204.742 | 1.725 | 0.101 | -75.284 781.778 |
| C(dose)[T.1] | -512.3362 | 240.958 | -2.126 | 0.047 | -1016.667 -8.006 |
| expression | -48.1299 | 32.942 | -1.461 | 0.160 | -117.078 20.818 |
| expression:C(dose)[T.1] | 89.5530 | 38.392 | 2.333 | 0.031 | 9.197 169.909 |
| Omnibus: | 0.284 | Durbin-Watson: | 1.680 |
| Prob(Omnibus): | 0.867 | Jarque-Bera (JB): | 0.105 |
| Skew: | -0.151 | Prob(JB): | 0.949 |
| Kurtosis: | 2.865 | Cond. No. | 582. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.79 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.82e-05 |
| Time: | 22:50:01 | Log-Likelihood: | -100.55 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.3868 | 116.357 | -0.485 | 0.633 | -299.104 186.330 |
| C(dose)[T.1] | 49.3549 | 9.544 | 5.171 | 0.000 | 29.447 69.263 |
| expression | 17.8002 | 18.703 | 0.952 | 0.353 | -21.214 56.814 |
| Omnibus: | 0.123 | Durbin-Watson: | 1.847 |
| Prob(Omnibus): | 0.940 | Jarque-Bera (JB): | 0.201 |
| Skew: | 0.145 | Prob(JB): | 0.904 |
| Kurtosis: | 2.646 | Cond. No. | 177. |
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: | 22:50:02 | 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: | 0.215 |
| Model: | OLS | Adj. R-squared: | 0.178 |
| Method: | Least Squares | F-statistic: | 5.763 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0257 |
| Time: | 22:50:02 | Log-Likelihood: | -110.32 |
| No. Observations: | 23 | AIC: | 224.6 |
| Df Residuals: | 21 | BIC: | 226.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -300.7845 | 158.633 | -1.896 | 0.072 | -630.680 29.111 |
| expression | 60.2046 | 25.079 | 2.401 | 0.026 | 8.050 112.360 |
| Omnibus: | 2.860 | Durbin-Watson: | 2.116 |
| Prob(Omnibus): | 0.239 | Jarque-Bera (JB): | 1.266 |
| Skew: | -0.072 | Prob(JB): | 0.531 |
| Kurtosis: | 1.860 | Cond. No. | 161. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.767 | 0.398 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.491 |
| Model: | OLS | Adj. R-squared: | 0.353 |
| Method: | Least Squares | F-statistic: | 3.543 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0516 |
| Time: | 22:50:02 | Log-Likelihood: | -70.229 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.1395 | 178.364 | -0.062 | 0.951 | -403.716 381.437 |
| C(dose)[T.1] | -102.7512 | 324.000 | -0.317 | 0.757 | -815.871 610.369 |
| expression | 13.9910 | 31.696 | 0.441 | 0.667 | -55.771 83.753 |
| expression:C(dose)[T.1] | 25.4489 | 56.062 | 0.454 | 0.659 | -97.942 148.840 |
| Omnibus: | 2.169 | Durbin-Watson: | 0.791 |
| Prob(Omnibus): | 0.338 | Jarque-Bera (JB): | 1.576 |
| Skew: | -0.629 | Prob(JB): | 0.455 |
| Kurtosis: | 2.031 | Cond. No. | 301. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.396 |
| Method: | Least Squares | F-statistic: | 5.581 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0193 |
| Time: | 22:50:02 | Log-Likelihood: | -70.368 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.8204 | 142.311 | -0.399 | 0.697 | -366.888 253.248 |
| C(dose)[T.1] | 44.1268 | 16.321 | 2.704 | 0.019 | 8.567 79.686 |
| expression | 22.1256 | 25.264 | 0.876 | 0.398 | -32.920 77.171 |
| Omnibus: | 2.419 | Durbin-Watson: | 0.923 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.830 |
| Skew: | -0.739 | Prob(JB): | 0.400 |
| Kurtosis: | 2.139 | Cond. No. | 111. |
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: | 22:50:02 | 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: | 0.166 |
| Model: | OLS | Adj. R-squared: | 0.102 |
| Method: | Least Squares | F-statistic: | 2.592 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.131 |
| Time: | 22:50:02 | Log-Likelihood: | -73.936 |
| No. Observations: | 15 | AIC: | 151.9 |
| Df Residuals: | 13 | BIC: | 153.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -172.3013 | 165.448 | -1.041 | 0.317 | -529.730 185.127 |
| expression | 46.3536 | 28.789 | 1.610 | 0.131 | -15.842 108.549 |
| Omnibus: | 0.545 | Durbin-Watson: | 1.804 |
| Prob(Omnibus): | 0.762 | Jarque-Bera (JB): | 0.557 |
| Skew: | 0.013 | Prob(JB): | 0.757 |
| Kurtosis: | 2.056 | Cond. No. | 106. |