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 |
| 4.735 | 0.042 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.762 |
| Model: | OLS | Adj. R-squared: | 0.725 |
| Method: | Least Squares | F-statistic: | 20.29 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.80e-06 |
| Time: | 23:02:22 | Log-Likelihood: | -96.590 |
| No. Observations: | 23 | AIC: | 201.2 |
| Df Residuals: | 19 | BIC: | 205.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.6989 | 54.008 | 1.679 | 0.109 | -22.340 203.738 |
| C(dose)[T.1] | 215.3882 | 86.853 | 2.480 | 0.023 | 33.602 397.174 |
| expression | -5.3460 | 7.877 | -0.679 | 0.506 | -21.832 11.140 |
| expression:C(dose)[T.1] | -24.8531 | 12.978 | -1.915 | 0.071 | -52.016 2.310 |
| Omnibus: | 1.748 | Durbin-Watson: | 2.122 |
| Prob(Omnibus): | 0.417 | Jarque-Bera (JB): | 1.433 |
| Skew: | 0.455 | Prob(JB): | 0.489 |
| Kurtosis: | 2.184 | Cond. No. | 198. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.688 |
| Method: | Least Squares | F-statistic: | 25.24 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.39e-06 |
| Time: | 23:02:22 | Log-Likelihood: | -98.619 |
| No. Observations: | 23 | AIC: | 203.2 |
| Df Residuals: | 20 | BIC: | 206.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 153.1898 | 45.815 | 3.344 | 0.003 | 57.621 248.759 |
| C(dose)[T.1] | 49.6920 | 8.062 | 6.164 | 0.000 | 32.875 66.509 |
| expression | -14.5011 | 6.664 | -2.176 | 0.042 | -28.403 -0.600 |
| Omnibus: | 1.058 | Durbin-Watson: | 1.782 |
| Prob(Omnibus): | 0.589 | Jarque-Bera (JB): | 0.942 |
| Skew: | 0.290 | Prob(JB): | 0.625 |
| Kurtosis: | 2.196 | Cond. No. | 80.3 |
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: | 23:02:22 | 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: | 0.177 |
| Model: | OLS | Adj. R-squared: | 0.138 |
| Method: | Least Squares | F-statistic: | 4.522 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0455 |
| Time: | 23:02:22 | Log-Likelihood: | -110.86 |
| No. Observations: | 23 | AIC: | 225.7 |
| Df Residuals: | 21 | BIC: | 228.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.1921 | 72.936 | 3.211 | 0.004 | 82.514 385.870 |
| expression | -23.0368 | 10.833 | -2.127 | 0.045 | -45.565 -0.508 |
| Omnibus: | 1.891 | Durbin-Watson: | 2.633 |
| Prob(Omnibus): | 0.389 | Jarque-Bera (JB): | 1.067 |
| Skew: | -0.112 | Prob(JB): | 0.587 |
| Kurtosis: | 1.969 | Cond. No. | 76.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.011 | 0.917 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.549 |
| Model: | OLS | Adj. R-squared: | 0.426 |
| Method: | Least Squares | F-statistic: | 4.464 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0278 |
| Time: | 23:02:23 | Log-Likelihood: | -69.327 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 11 | BIC: | 149.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 219.4989 | 160.687 | 1.366 | 0.199 | -134.171 573.168 |
| C(dose)[T.1] | -412.0566 | 296.221 | -1.391 | 0.192 | -1064.034 239.921 |
| expression | -22.1376 | 23.338 | -0.949 | 0.363 | -73.505 29.230 |
| expression:C(dose)[T.1] | 66.4997 | 42.631 | 1.560 | 0.147 | -27.332 160.331 |
| Omnibus: | 4.735 | Durbin-Watson: | 0.837 |
| Prob(Omnibus): | 0.094 | Jarque-Bera (JB): | 2.221 |
| Skew: | -0.868 | Prob(JB): | 0.329 |
| Kurtosis: | 3.737 | Cond. No. | 346. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.895 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0279 |
| Time: | 23:02:23 | Log-Likelihood: | -70.826 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 82.5946 | 142.412 | 0.580 | 0.573 | -227.694 392.884 |
| C(dose)[T.1] | 49.4176 | 15.868 | 3.114 | 0.009 | 14.845 83.991 |
| expression | -2.2078 | 20.664 | -0.107 | 0.917 | -47.231 42.815 |
| Omnibus: | 2.566 | Durbin-Watson: | 0.805 |
| Prob(Omnibus): | 0.277 | Jarque-Bera (JB): | 1.795 |
| Skew: | -0.821 | Prob(JB): | 0.408 |
| Kurtosis: | 2.581 | Cond. No. | 129. |
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: | 23:02:23 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.05466 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.819 |
| Time: | 23:02:23 | Log-Likelihood: | -75.269 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.8264 | 183.518 | 0.277 | 0.786 | -345.640 447.293 |
| expression | 6.1883 | 26.469 | 0.234 | 0.819 | -50.994 63.371 |
| Omnibus: | 0.309 | Durbin-Watson: | 1.625 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.457 |
| Skew: | -0.065 | Prob(JB): | 0.796 |
| Kurtosis: | 2.155 | Cond. No. | 128. |