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 |
| 0.677 | 0.420 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 13.66 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.52e-05 |
| Time: | 22:54:33 | Log-Likelihood: | -99.885 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 19 | BIC: | 212.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.5814 | 30.054 | 3.081 | 0.006 | 29.678 155.484 |
| C(dose)[T.1] | -11.5095 | 54.141 | -0.213 | 0.834 | -124.829 101.810 |
| expression | -10.3363 | 7.937 | -1.302 | 0.208 | -26.949 6.276 |
| expression:C(dose)[T.1] | 18.4761 | 15.847 | 1.166 | 0.258 | -14.692 51.644 |
| Omnibus: | 1.121 | Durbin-Watson: | 1.846 |
| Prob(Omnibus): | 0.571 | Jarque-Bera (JB): | 0.827 |
| Skew: | -0.070 | Prob(JB): | 0.661 |
| Kurtosis: | 2.082 | Cond. No. | 56.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 19.46 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.03e-05 |
| Time: | 22:54:33 | Log-Likelihood: | -100.68 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.3741 | 26.414 | 2.854 | 0.010 | 20.275 130.473 |
| C(dose)[T.1] | 50.7137 | 9.196 | 5.515 | 0.000 | 31.531 69.896 |
| expression | -5.7013 | 6.931 | -0.823 | 0.420 | -20.160 8.757 |
| Omnibus: | 0.066 | Durbin-Watson: | 1.905 |
| Prob(Omnibus): | 0.967 | Jarque-Bera (JB): | 0.278 |
| Skew: | -0.066 | Prob(JB): | 0.870 |
| Kurtosis: | 2.478 | Cond. No. | 23.8 |
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: | 22:54:33 | 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: | 0.144 |
| Model: | OLS | Adj. R-squared: | 0.104 |
| Method: | Least Squares | F-statistic: | 3.543 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0737 |
| Time: | 22:54:33 | Log-Likelihood: | -111.31 |
| No. Observations: | 23 | AIC: | 226.6 |
| Df Residuals: | 21 | BIC: | 228.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.9277 | 35.805 | 4.076 | 0.001 | 71.466 220.389 |
| expression | -18.9584 | 10.073 | -1.882 | 0.074 | -39.906 1.989 |
| Omnibus: | 2.443 | Durbin-Watson: | 2.620 |
| Prob(Omnibus): | 0.295 | Jarque-Bera (JB): | 1.490 |
| Skew: | 0.358 | Prob(JB): | 0.475 |
| Kurtosis: | 1.979 | Cond. No. | 20.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.053 | 0.822 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.304 |
| Method: | Least Squares | F-statistic: | 3.040 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0746 |
| Time: | 22:54:33 | Log-Likelihood: | -70.772 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.3597 | 118.001 | 0.274 | 0.789 | -227.360 292.079 |
| C(dose)[T.1] | 81.3569 | 160.985 | 0.505 | 0.623 | -272.968 435.682 |
| expression | 12.1550 | 40.689 | 0.299 | 0.771 | -77.402 101.712 |
| expression:C(dose)[T.1] | -11.1636 | 55.087 | -0.203 | 0.843 | -132.409 110.081 |
| Omnibus: | 2.753 | Durbin-Watson: | 0.865 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.848 |
| Skew: | -0.844 | Prob(JB): | 0.397 |
| Kurtosis: | 2.672 | Cond. No. | 87.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.933 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0273 |
| Time: | 22:54:33 | Log-Likelihood: | -70.800 |
| 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 | 49.9324 | 76.769 | 0.650 | 0.528 | -117.333 217.198 |
| C(dose)[T.1] | 48.9028 | 15.757 | 3.104 | 0.009 | 14.572 83.233 |
| expression | 6.0643 | 26.310 | 0.230 | 0.822 | -51.260 63.389 |
| Omnibus: | 2.754 | Durbin-Watson: | 0.861 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.836 |
| Skew: | -0.842 | Prob(JB): | 0.399 |
| Kurtosis: | 2.684 | Cond. No. | 32.5 |
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: | 22:54:33 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.065 |
| Method: | Least Squares | F-statistic: | 0.1402 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.714 |
| Time: | 22:54:34 | Log-Likelihood: | -75.220 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 56.7936 | 98.989 | 0.574 | 0.576 | -157.060 270.647 |
| expression | 12.6670 | 33.828 | 0.374 | 0.714 | -60.414 85.748 |
| Omnibus: | 0.506 | Durbin-Watson: | 1.648 |
| Prob(Omnibus): | 0.777 | Jarque-Bera (JB): | 0.546 |
| Skew: | 0.066 | Prob(JB): | 0.761 |
| Kurtosis: | 2.075 | Cond. No. | 32.0 |