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 |
| 0.081 | 0.779 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000104 |
| Time: | 23:03:51 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.1381 | 37.671 | 2.207 | 0.040 | 4.291 161.985 |
| C(dose)[T.1] | 17.5975 | 48.419 | 0.363 | 0.720 | -83.744 118.939 |
| expression | -7.4176 | 9.531 | -0.778 | 0.446 | -27.366 12.530 |
| expression:C(dose)[T.1] | 9.0462 | 11.892 | 0.761 | 0.456 | -15.844 33.937 |
| Omnibus: | 0.663 | Durbin-Watson: | 1.971 |
| Prob(Omnibus): | 0.718 | Jarque-Bera (JB): | 0.656 |
| Skew: | 0.074 | Prob(JB): | 0.720 |
| Kurtosis: | 2.186 | Cond. No. | 65.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.61 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.72e-05 |
| Time: | 23:03:51 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.4769 | 22.814 | 2.651 | 0.015 | 12.888 108.066 |
| C(dose)[T.1] | 53.7887 | 8.894 | 6.047 | 0.000 | 35.235 72.342 |
| expression | -1.6073 | 5.640 | -0.285 | 0.779 | -13.372 10.157 |
| Omnibus: | 0.333 | Durbin-Watson: | 1.910 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.489 |
| Skew: | -0.002 | Prob(JB): | 0.783 |
| Kurtosis: | 2.285 | Cond. No. | 22.8 |
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: | 23:03:51 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2408 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.629 |
| Time: | 23:03:51 | Log-Likelihood: | -112.97 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.6840 | 37.443 | 1.647 | 0.114 | -16.184 139.552 |
| expression | 4.4697 | 9.109 | 0.491 | 0.629 | -14.473 23.412 |
| Omnibus: | 1.979 | Durbin-Watson: | 2.466 |
| Prob(Omnibus): | 0.372 | Jarque-Bera (JB): | 1.259 |
| Skew: | 0.290 | Prob(JB): | 0.533 |
| Kurtosis: | 2.011 | Cond. No. | 22.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.624 | 0.445 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.633 |
| Model: | OLS | Adj. R-squared: | 0.533 |
| Method: | Least Squares | F-statistic: | 6.330 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00942 |
| Time: | 23:03:52 | Log-Likelihood: | -67.778 |
| No. Observations: | 15 | AIC: | 143.6 |
| Df Residuals: | 11 | BIC: | 146.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.3922 | 33.790 | 3.859 | 0.003 | 56.021 204.763 |
| C(dose)[T.1] | -73.3814 | 57.157 | -1.284 | 0.226 | -199.184 52.421 |
| expression | -13.1023 | 6.730 | -1.947 | 0.078 | -27.914 1.710 |
| expression:C(dose)[T.1] | 26.6362 | 12.268 | 2.171 | 0.053 | -0.365 53.637 |
| Omnibus: | 3.425 | Durbin-Watson: | 1.572 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.233 |
| Skew: | -0.552 | Prob(JB): | 0.540 |
| Kurtosis: | 3.867 | Cond. No. | 51.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.389 |
| Method: | Least Squares | F-statistic: | 5.451 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0207 |
| Time: | 23:03:52 | Log-Likelihood: | -70.453 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.8730 | 32.909 | 2.792 | 0.016 | 20.170 163.576 |
| C(dose)[T.1] | 47.1581 | 15.561 | 3.031 | 0.010 | 13.253 81.063 |
| expression | -5.0867 | 6.439 | -0.790 | 0.445 | -19.116 8.942 |
| Omnibus: | 2.575 | Durbin-Watson: | 1.051 |
| Prob(Omnibus): | 0.276 | Jarque-Bera (JB): | 1.584 |
| Skew: | -0.790 | Prob(JB): | 0.453 |
| Kurtosis: | 2.802 | Cond. No. | 21.7 |
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: | 23:03:52 | 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: | 0.075 |
| Model: | OLS | Adj. R-squared: | 0.004 |
| Method: | Least Squares | F-statistic: | 1.054 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.323 |
| Time: | 23:03:52 | Log-Likelihood: | -74.715 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 131.8807 | 38.482 | 3.427 | 0.005 | 48.746 215.015 |
| expression | -8.3222 | 8.106 | -1.027 | 0.323 | -25.834 9.189 |
| Omnibus: | 3.478 | Durbin-Watson: | 1.655 |
| Prob(Omnibus): | 0.176 | Jarque-Bera (JB): | 1.706 |
| Skew: | 0.525 | Prob(JB): | 0.426 |
| Kurtosis: | 1.724 | Cond. No. | 19.5 |