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 |
| 0.452 | 0.509 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.17 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.000113 |
| Time: | 00:13:08 | Log-Likelihood: | -100.78 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.0101 | 169.659 | 0.914 | 0.372 | -200.091 510.111 |
| C(dose)[T.1] | 3.5384 | 222.293 | 0.016 | 0.987 | -461.725 468.802 |
| expression | -13.8851 | 23.355 | -0.595 | 0.559 | -62.767 34.997 |
| expression:C(dose)[T.1] | 6.8272 | 30.655 | 0.223 | 0.826 | -57.333 70.988 |
| Omnibus: | 0.104 | Durbin-Watson: | 1.957 |
| Prob(Omnibus): | 0.950 | Jarque-Bera (JB): | 0.323 |
| Skew: | -0.059 | Prob(JB): | 0.851 |
| Kurtosis: | 2.431 | Cond. No. | 497. |
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: | 19.14 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.27e-05 |
| Time: | 00:13:08 | 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 | 126.2413 | 107.348 | 1.176 | 0.253 | -97.684 350.166 |
| C(dose)[T.1] | 53.0066 | 8.686 | 6.102 | 0.000 | 34.887 71.126 |
| expression | -9.9223 | 14.764 | -0.672 | 0.509 | -40.719 20.874 |
| Omnibus: | 0.035 | Durbin-Watson: | 1.927 |
| Prob(Omnibus): | 0.983 | Jarque-Bera (JB): | 0.216 |
| Skew: | -0.070 | Prob(JB): | 0.898 |
| Kurtosis: | 2.546 | Cond. No. | 183. |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 00:13:08 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.029 |
| Method: | Least Squares | F-statistic: | 0.3811 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.544 |
| Time: | 00:13:08 | Log-Likelihood: | -112.90 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 188.5424 | 176.422 | 1.069 | 0.297 | -178.348 555.432 |
| expression | -15.0232 | 24.335 | -0.617 | 0.544 | -65.631 35.584 |
| Omnibus: | 3.609 | Durbin-Watson: | 2.631 |
| Prob(Omnibus): | 0.165 | Jarque-Bera (JB): | 1.589 |
| Skew: | 0.264 | Prob(JB): | 0.452 |
| Kurtosis: | 1.825 | Cond. No. | 182. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.076 | 0.787 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 3.603 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0495 |
| Time: | 00:13:08 | Log-Likelihood: | -70.167 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 157.0280 | 93.092 | 1.687 | 0.120 | -47.867 361.923 |
| C(dose)[T.1] | -55.9093 | 108.409 | -0.516 | 0.616 | -294.515 182.697 |
| expression | -14.6606 | 15.116 | -0.970 | 0.353 | -47.930 18.609 |
| expression:C(dose)[T.1] | 17.3628 | 17.852 | 0.973 | 0.352 | -21.930 56.655 |
| Omnibus: | 2.372 | Durbin-Watson: | 0.645 |
| Prob(Omnibus): | 0.305 | Jarque-Bera (JB): | 1.593 |
| Skew: | -0.779 | Prob(JB): | 0.451 |
| Kurtosis: | 2.650 | Cond. No. | 124. |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0270 |
| Time: | 00:13:08 | 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 | 80.9521 | 50.361 | 1.607 | 0.134 | -28.775 190.679 |
| C(dose)[T.1] | 48.3709 | 15.973 | 3.028 | 0.010 | 13.569 83.173 |
| expression | -2.2128 | 8.024 | -0.276 | 0.787 | -19.696 15.270 |
| Omnibus: | 2.436 | Durbin-Watson: | 0.796 |
| Prob(Omnibus): | 0.296 | Jarque-Bera (JB): | 1.716 |
| Skew: | -0.799 | Prob(JB): | 0.424 |
| Kurtosis: | 2.564 | Cond. No. | 40.0 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 00:13:08 | 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: | 0.034 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.4526 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.513 |
| Time: | 00:13:08 | Log-Likelihood: | -75.043 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.6759 | 60.304 | 2.217 | 0.045 | 3.396 263.955 |
| expression | -6.7668 | 10.058 | -0.673 | 0.513 | -28.497 14.963 |
| Omnibus: | 1.234 | Durbin-Watson: | 1.620 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 0.854 |
| Skew: | 0.245 | Prob(JB): | 0.653 |
| Kurtosis: | 1.939 | Cond. No. | 37.2 |