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 |
| 1.814 | 0.193 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.726 |
| Model: | OLS | Adj. R-squared: | 0.683 |
| Method: | Least Squares | F-statistic: | 16.81 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.41e-05 |
| Time: | 22:50:41 | Log-Likelihood: | -98.203 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 19 | BIC: | 208.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.2942 | 252.876 | 0.464 | 0.648 | -411.981 646.569 |
| C(dose)[T.1] | -626.6156 | 368.463 | -1.701 | 0.105 | -1397.818 144.586 |
| expression | -7.5214 | 30.142 | -0.250 | 0.806 | -70.609 55.566 |
| expression:C(dose)[T.1] | 79.1115 | 43.306 | 1.827 | 0.083 | -11.528 169.751 |
| Omnibus: | 2.407 | Durbin-Watson: | 1.811 |
| Prob(Omnibus): | 0.300 | Jarque-Bera (JB): | 1.865 |
| Skew: | 0.683 | Prob(JB): | 0.394 |
| Kurtosis: | 2.713 | Cond. No. | 1.03e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.646 |
| Method: | Least Squares | F-statistic: | 21.08 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.19e-05 |
| Time: | 22:50:41 | Log-Likelihood: | -100.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 20 | BIC: | 209.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -204.1624 | 191.928 | -1.064 | 0.300 | -604.518 196.193 |
| C(dose)[T.1] | 46.2802 | 9.898 | 4.676 | 0.000 | 25.633 66.927 |
| expression | 30.8040 | 22.872 | 1.347 | 0.193 | -16.906 78.514 |
| Omnibus: | 0.765 | Durbin-Watson: | 1.755 |
| Prob(Omnibus): | 0.682 | Jarque-Bera (JB): | 0.724 |
| Skew: | 0.374 | Prob(JB): | 0.696 |
| Kurtosis: | 2.557 | Cond. No. | 395. |
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: | 22:50:41 | 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: | 0.327 |
| Model: | OLS | Adj. R-squared: | 0.294 |
| Method: | Least Squares | F-statistic: | 10.18 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00440 |
| Time: | 22:50:41 | Log-Likelihood: | -108.56 |
| No. Observations: | 23 | AIC: | 221.1 |
| Df Residuals: | 21 | BIC: | 223.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -663.0791 | 232.869 | -2.847 | 0.010 | -1147.357 -178.801 |
| expression | 87.4174 | 27.397 | 3.191 | 0.004 | 30.443 144.392 |
| Omnibus: | 0.100 | Durbin-Watson: | 2.373 |
| Prob(Omnibus): | 0.951 | Jarque-Bera (JB): | 0.103 |
| Skew: | -0.098 | Prob(JB): | 0.950 |
| Kurtosis: | 2.737 | Cond. No. | 339. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.767 | 0.398 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.527 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 4.089 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0354 |
| Time: | 22:50:41 | Log-Likelihood: | -69.681 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.9986 | 202.194 | 0.163 | 0.873 | -412.027 478.024 |
| C(dose)[T.1] | -361.2079 | 388.738 | -0.929 | 0.373 | -1216.814 494.398 |
| expression | 4.2853 | 25.128 | 0.171 | 0.868 | -51.021 59.592 |
| expression:C(dose)[T.1] | 47.0877 | 45.828 | 1.027 | 0.326 | -53.780 147.955 |
| Omnibus: | 2.800 | Durbin-Watson: | 0.621 |
| Prob(Omnibus): | 0.247 | Jarque-Bera (JB): | 2.030 |
| Skew: | -0.765 | Prob(JB): | 0.362 |
| Kurtosis: | 2.047 | Cond. No. | 536. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.396 |
| Method: | Least Squares | F-statistic: | 5.580 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0193 |
| Time: | 22:50:41 | Log-Likelihood: | -70.369 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.7405 | 169.592 | -0.476 | 0.643 | -450.250 288.769 |
| C(dose)[T.1] | 37.6786 | 20.147 | 1.870 | 0.086 | -6.218 81.575 |
| expression | 18.4419 | 21.063 | 0.876 | 0.398 | -27.450 64.334 |
| Omnibus: | 2.925 | Durbin-Watson: | 0.553 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 2.015 |
| Skew: | -0.735 | Prob(JB): | 0.365 |
| Kurtosis: | 1.970 | Cond. No. | 190. |
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: | 22:50:41 | 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: | 0.331 |
| Model: | OLS | Adj. R-squared: | 0.279 |
| Method: | Least Squares | F-statistic: | 6.428 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0249 |
| Time: | 22:50:41 | Log-Likelihood: | -72.287 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 13 | BIC: | 150.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -275.8529 | 145.985 | -1.890 | 0.081 | -591.235 39.529 |
| expression | 44.1615 | 17.418 | 2.535 | 0.025 | 6.531 81.792 |
| Omnibus: | 2.007 | Durbin-Watson: | 0.857 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.239 |
| Skew: | -0.429 | Prob(JB): | 0.538 |
| Kurtosis: | 1.883 | Cond. No. | 149. |