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 |
| 5.281 | 0.032 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.734 |
| Model: | OLS | Adj. R-squared: | 0.692 |
| Method: | Least Squares | F-statistic: | 17.44 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.10e-05 |
| Time: | 10:02:13 | Log-Likelihood: | -97.894 |
| No. Observations: | 23 | AIC: | 203.8 |
| Df Residuals: | 19 | BIC: | 208.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -42.2927 | 108.389 | -0.390 | 0.701 | -269.154 184.569 |
| C(dose)[T.1] | -61.5105 | 142.533 | -0.432 | 0.671 | -359.836 236.815 |
| expression | 12.9230 | 14.497 | 0.891 | 0.384 | -17.419 43.265 |
| expression:C(dose)[T.1] | 17.6300 | 19.711 | 0.894 | 0.382 | -23.626 58.886 |
| Omnibus: | 4.209 | Durbin-Watson: | 1.718 |
| Prob(Omnibus): | 0.122 | Jarque-Bera (JB): | 1.553 |
| Skew: | 0.147 | Prob(JB): | 0.460 |
| Kurtosis: | 1.761 | Cond. No. | 354. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.695 |
| Method: | Least Squares | F-statistic: | 26.02 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.72e-06 |
| Time: | 10:02:13 | Log-Likelihood: | -98.368 |
| No. Observations: | 23 | AIC: | 202.7 |
| Df Residuals: | 20 | BIC: | 206.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -113.5027 | 73.180 | -1.551 | 0.137 | -266.154 39.149 |
| C(dose)[T.1] | 65.6881 | 9.473 | 6.934 | 0.000 | 45.928 85.448 |
| expression | 22.4592 | 9.773 | 2.298 | 0.032 | 2.072 42.846 |
| Omnibus: | 5.787 | Durbin-Watson: | 1.866 |
| Prob(Omnibus): | 0.055 | Jarque-Bera (JB): | 1.712 |
| Skew: | 0.024 | Prob(JB): | 0.425 |
| Kurtosis: | 1.664 | Cond. No. | 139. |
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: | 10:02:13 | 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: | 0.055 |
| Model: | OLS | Adj. R-squared: | 0.010 |
| Method: | Least Squares | F-statistic: | 1.218 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.282 |
| Time: | 10:02:13 | Log-Likelihood: | -112.46 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 194.9461 | 104.635 | 1.863 | 0.076 | -22.655 412.547 |
| expression | -15.9943 | 14.491 | -1.104 | 0.282 | -46.130 14.142 |
| Omnibus: | 2.967 | Durbin-Watson: | 2.340 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 1.691 |
| Skew: | 0.395 | Prob(JB): | 0.429 |
| Kurtosis: | 1.932 | Cond. No. | 110. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.040 | 0.845 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.301 |
| Method: | Least Squares | F-statistic: | 3.012 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0762 |
| Time: | 10:02:13 | Log-Likelihood: | -70.803 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.5912 | 201.986 | 0.127 | 0.901 | -418.977 470.159 |
| C(dose)[T.1] | 82.3286 | 383.510 | 0.215 | 0.834 | -761.770 926.428 |
| expression | 5.7595 | 27.757 | 0.207 | 0.839 | -55.334 66.853 |
| expression:C(dose)[T.1] | -4.5390 | 53.454 | -0.085 | 0.934 | -122.191 113.113 |
| Omnibus: | 3.030 | Durbin-Watson: | 0.790 |
| Prob(Omnibus): | 0.220 | Jarque-Bera (JB): | 2.062 |
| Skew: | -0.892 | Prob(JB): | 0.357 |
| Kurtosis: | 2.661 | Cond. No. | 416. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.921 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0275 |
| Time: | 10:02:13 | Log-Likelihood: | -70.808 |
| 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 | 34.4818 | 165.431 | 0.208 | 0.838 | -325.962 394.925 |
| C(dose)[T.1] | 49.7942 | 15.996 | 3.113 | 0.009 | 14.941 84.647 |
| expression | 4.5356 | 22.719 | 0.200 | 0.845 | -44.965 54.037 |
| Omnibus: | 2.936 | Durbin-Watson: | 0.776 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 2.039 |
| Skew: | -0.882 | Prob(JB): | 0.361 |
| Kurtosis: | 2.612 | Cond. No. | 155. |
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: | 10:02:13 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.069 |
| Method: | Least Squares | F-statistic: | 0.09115 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.768 |
| Time: | 10:02:13 | Log-Likelihood: | -75.248 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.2743 | 207.623 | 0.753 | 0.465 | -292.267 604.816 |
| expression | -8.7031 | 28.827 | -0.302 | 0.768 | -70.981 53.575 |
| Omnibus: | 0.738 | Durbin-Watson: | 1.604 |
| Prob(Omnibus): | 0.691 | Jarque-Bera (JB): | 0.629 |
| Skew: | 0.051 | Prob(JB): | 0.730 |
| Kurtosis: | 2.002 | Cond. No. | 151. |