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 |
| 0.183 | 0.673 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.32 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.000105 |
| Time: | 04:51:12 | Log-Likelihood: | -100.68 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.8157 | 66.023 | 0.664 | 0.515 | -94.373 182.004 |
| C(dose)[T.1] | 118.9783 | 96.940 | 1.227 | 0.235 | -83.919 321.876 |
| expression | 1.8752 | 11.862 | 0.158 | 0.876 | -22.952 26.702 |
| expression:C(dose)[T.1] | -11.6837 | 17.269 | -0.677 | 0.507 | -47.828 24.460 |
| Omnibus: | 0.058 | Durbin-Watson: | 1.798 |
| Prob(Omnibus): | 0.971 | Jarque-Bera (JB): | 0.281 |
| Skew: | 0.010 | Prob(JB): | 0.869 |
| Kurtosis: | 2.459 | Cond. No. | 162. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.76 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 2.59e-05 |
| Time: | 04:51:12 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.3671 | 47.509 | 1.565 | 0.133 | -24.736 173.470 |
| C(dose)[T.1] | 53.6670 | 8.764 | 6.124 | 0.000 | 35.386 71.948 |
| expression | -3.6374 | 8.503 | -0.428 | 0.673 | -21.374 14.100 |
| Omnibus: | 0.505 | Durbin-Watson: | 1.838 |
| Prob(Omnibus): | 0.777 | Jarque-Bera (JB): | 0.580 |
| Skew: | 0.046 | Prob(JB): | 0.748 |
| Kurtosis: | 2.227 | Cond. No. | 63.3 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:51:12 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004537 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.947 |
| Time: | 04:51:12 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.4446 | 78.613 | 0.947 | 0.354 | -89.041 237.930 |
| expression | 0.9440 | 14.015 | 0.067 | 0.947 | -28.202 30.090 |
| Omnibus: | 3.257 | Durbin-Watson: | 2.480 |
| Prob(Omnibus): | 0.196 | Jarque-Bera (JB): | 1.570 |
| Skew: | 0.296 | Prob(JB): | 0.456 |
| Kurtosis: | 1.865 | Cond. No. | 63.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.014 | 0.908 | 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: | 3.035 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0748 |
| Time: | 04:51:12 | Log-Likelihood: | -70.777 |
| 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 | 96.3084 | 205.379 | 0.469 | 0.648 | -355.727 548.344 |
| C(dose)[T.1] | -15.2461 | 249.635 | -0.061 | 0.952 | -564.689 534.197 |
| expression | -5.3962 | 38.310 | -0.141 | 0.891 | -89.716 78.923 |
| expression:C(dose)[T.1] | 12.8353 | 48.408 | 0.265 | 0.796 | -93.710 119.380 |
| Omnibus: | 2.446 | Durbin-Watson: | 0.856 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.709 |
| Skew: | -0.800 | Prob(JB): | 0.425 |
| Kurtosis: | 2.580 | Cond. No. | 226. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.897 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0279 |
| Time: | 04:51:12 | Log-Likelihood: | -70.824 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.2853 | 120.929 | 0.441 | 0.667 | -210.196 316.767 |
| C(dose)[T.1] | 50.7063 | 20.313 | 2.496 | 0.028 | 6.448 94.964 |
| expression | 2.6427 | 22.493 | 0.117 | 0.908 | -46.366 51.652 |
| Omnibus: | 2.765 | Durbin-Watson: | 0.806 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.922 |
| Skew: | -0.854 | Prob(JB): | 0.383 |
| Kurtosis: | 2.602 | Cond. No. | 81.8 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 04:51:12 | 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: | 0.163 |
| Model: | OLS | Adj. R-squared: | 0.099 |
| Method: | Least Squares | F-statistic: | 2.541 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.135 |
| Time: | 04:51:12 | Log-Likelihood: | -73.961 |
| No. Observations: | 15 | AIC: | 151.9 |
| Df Residuals: | 13 | BIC: | 153.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 259.6270 | 104.528 | 2.484 | 0.027 | 33.807 485.447 |
| expression | -32.8819 | 20.628 | -1.594 | 0.135 | -77.447 11.683 |
| Omnibus: | 0.325 | Durbin-Watson: | 1.332 |
| Prob(Omnibus): | 0.850 | Jarque-Bera (JB): | 0.472 |
| Skew: | 0.194 | Prob(JB): | 0.790 |
| Kurtosis: | 2.223 | Cond. No. | 59.2 |