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 |
| 0.115 | 0.738 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.39 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.000101 |
| Time: | 14:37:09 | Log-Likelihood: | -100.64 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -29.2521 | 159.657 | -0.183 | 0.857 | -363.417 304.913 |
| C(dose)[T.1] | 536.0640 | 620.114 | 0.864 | 0.398 | -761.849 1833.977 |
| expression | 9.2710 | 17.722 | 0.523 | 0.607 | -27.822 46.364 |
| expression:C(dose)[T.1] | -54.1742 | 69.677 | -0.778 | 0.446 | -200.010 91.662 |
| Omnibus: | 0.136 | Durbin-Watson: | 2.022 |
| Prob(Omnibus): | 0.934 | Jarque-Bera (JB): | 0.354 |
| Skew: | 0.056 | Prob(JB): | 0.838 |
| Kurtosis: | 2.403 | Cond. No. | 1.44e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.66 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 2.68e-05 |
| Time: | 14:37:09 | Log-Likelihood: | -101.00 |
| 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 | 2.2974 | 152.879 | 0.015 | 0.988 | -316.604 321.198 |
| C(dose)[T.1] | 53.9751 | 8.944 | 6.035 | 0.000 | 35.318 72.632 |
| expression | 5.7664 | 16.969 | 0.340 | 0.738 | -29.630 41.163 |
| Omnibus: | 0.422 | Durbin-Watson: | 1.895 |
| Prob(Omnibus): | 0.810 | Jarque-Bera (JB): | 0.539 |
| Skew: | 0.041 | Prob(JB): | 0.764 |
| Kurtosis: | 2.255 | Cond. No. | 318. |
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, 30 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 14:37:09 | 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: | 0.016 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.3346 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.569 |
| Time: | 14:37:09 | Log-Likelihood: | -112.92 |
| 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 | 220.4909 | 243.476 | 0.906 | 0.375 | -285.844 726.826 |
| expression | -15.7299 | 27.194 | -0.578 | 0.569 | -72.283 40.823 |
| Omnibus: | 2.377 | Durbin-Watson: | 2.465 |
| Prob(Omnibus): | 0.305 | Jarque-Bera (JB): | 1.253 |
| Skew: | 0.199 | Prob(JB): | 0.534 |
| Kurtosis: | 1.928 | Cond. No. | 308. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.462 | 0.510 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.587 |
| Model: | OLS | Adj. R-squared: | 0.475 |
| Method: | Least Squares | F-statistic: | 5.219 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.0175 |
| Time: | 14:37:09 | Log-Likelihood: | -68.661 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 11 | BIC: | 148.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -392.2342 | 257.675 | -1.522 | 0.156 | -959.373 174.904 |
| C(dose)[T.1] | 678.4375 | 350.992 | 1.933 | 0.079 | -94.091 1450.966 |
| expression | 50.9269 | 28.525 | 1.785 | 0.102 | -11.856 113.710 |
| expression:C(dose)[T.1] | -70.7704 | 39.877 | -1.775 | 0.104 | -158.538 16.997 |
| Omnibus: | 0.749 | Durbin-Watson: | 1.659 |
| Prob(Omnibus): | 0.688 | Jarque-Bera (JB): | 0.598 |
| Skew: | -0.429 | Prob(JB): | 0.741 |
| Kurtosis: | 2.530 | Cond. No. | 588. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.381 |
| Method: | Least Squares | F-statistic: | 5.304 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.0224 |
| Time: | 14:37:09 | Log-Likelihood: | -70.550 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -65.3738 | 195.689 | -0.334 | 0.744 | -491.744 360.996 |
| C(dose)[T.1] | 56.2616 | 18.617 | 3.022 | 0.011 | 15.700 96.823 |
| expression | 14.7134 | 21.645 | 0.680 | 0.510 | -32.446 61.873 |
| Omnibus: | 5.986 | Durbin-Watson: | 0.924 |
| Prob(Omnibus): | 0.050 | Jarque-Bera (JB): | 3.302 |
| Skew: | -1.108 | Prob(JB): | 0.192 |
| Kurtosis: | 3.611 | Cond. No. | 227. |
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, 30 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 14:37:09 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | -0.007 |
| Method: | Least Squares | F-statistic: | 0.9071 |
| Date: | Thu, 30 Oct 2025 | Prob (F-statistic): | 0.358 |
| Time: | 14:37:09 | Log-Likelihood: | -74.794 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 284.9039 | 201.033 | 1.417 | 0.180 | -149.401 719.208 |
| expression | -21.8062 | 22.896 | -0.952 | 0.358 | -71.270 27.657 |
| Omnibus: | 0.372 | Durbin-Watson: | 1.277 |
| Prob(Omnibus): | 0.830 | Jarque-Bera (JB): | 0.220 |
| Skew: | -0.255 | Prob(JB): | 0.896 |
| Kurtosis: | 2.698 | Cond. No. | 182. |