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 |
| 0.247 | 0.625 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 13.67 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.49e-05 |
| Time: | 22:45:53 | Log-Likelihood: | -99.880 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 19 | BIC: | 212.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 221.5830 | 173.715 | 1.276 | 0.217 | -142.006 585.172 |
| C(dose)[T.1] | -592.3749 | 482.514 | -1.228 | 0.235 | -1602.288 417.538 |
| expression | -17.3710 | 18.019 | -0.964 | 0.347 | -55.084 20.342 |
| expression:C(dose)[T.1] | 65.8642 | 49.061 | 1.343 | 0.195 | -36.821 168.550 |
| Omnibus: | 0.056 | Durbin-Watson: | 2.102 |
| Prob(Omnibus): | 0.972 | Jarque-Bera (JB): | 0.271 |
| Skew: | -0.048 | Prob(JB): | 0.873 |
| Kurtosis: | 2.477 | Cond. No. | 1.28e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.85 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.51e-05 |
| Time: | 22:45:53 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.9809 | 164.798 | 0.825 | 0.419 | -207.782 479.744 |
| C(dose)[T.1] | 55.2782 | 9.553 | 5.787 | 0.000 | 35.351 75.205 |
| expression | -8.4868 | 17.092 | -0.497 | 0.625 | -44.140 27.167 |
| Omnibus: | 0.565 | Durbin-Watson: | 2.011 |
| Prob(Omnibus): | 0.754 | Jarque-Bera (JB): | 0.616 |
| Skew: | 0.091 | Prob(JB): | 0.735 |
| Kurtosis: | 2.219 | Cond. No. | 373. |
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: | 22:45:53 | 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: | 0.073 |
| Model: | OLS | Adj. R-squared: | 0.029 |
| Method: | Least Squares | F-statistic: | 1.652 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.213 |
| Time: | 22:45:54 | Log-Likelihood: | -112.23 |
| No. Observations: | 23 | AIC: | 228.5 |
| Df Residuals: | 21 | BIC: | 230.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -231.9935 | 242.633 | -0.956 | 0.350 | -736.576 272.589 |
| expression | 31.9878 | 24.889 | 1.285 | 0.213 | -19.771 83.747 |
| Omnibus: | 0.591 | Durbin-Watson: | 2.321 |
| Prob(Omnibus): | 0.744 | Jarque-Bera (JB): | 0.626 |
| Skew: | -0.080 | Prob(JB): | 0.731 |
| Kurtosis: | 2.208 | Cond. No. | 344. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.391 | 0.544 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.529 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 4.119 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0348 |
| Time: | 22:45:54 | Log-Likelihood: | -69.653 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 544.4911 | 348.904 | 1.561 | 0.147 | -223.441 1312.423 |
| C(dose)[T.1] | -439.5430 | 404.004 | -1.088 | 0.300 | -1328.749 449.663 |
| expression | -59.0749 | 43.183 | -1.368 | 0.199 | -154.120 35.970 |
| expression:C(dose)[T.1] | 60.5135 | 49.928 | 1.212 | 0.251 | -49.377 170.405 |
| Omnibus: | 2.877 | Durbin-Watson: | 0.888 |
| Prob(Omnibus): | 0.237 | Jarque-Bera (JB): | 1.640 |
| Skew: | -0.810 | Prob(JB): | 0.440 |
| Kurtosis: | 2.948 | Cond. No. | 651. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 5.239 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0231 |
| Time: | 22:45:54 | Log-Likelihood: | -70.593 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.9281 | 178.784 | 1.001 | 0.337 | -210.609 568.465 |
| C(dose)[T.1] | 49.7672 | 15.517 | 3.207 | 0.008 | 15.960 83.575 |
| expression | -13.8071 | 22.095 | -0.625 | 0.544 | -61.947 34.333 |
| Omnibus: | 2.565 | Durbin-Watson: | 0.778 |
| Prob(Omnibus): | 0.277 | Jarque-Bera (JB): | 1.881 |
| Skew: | -0.826 | Prob(JB): | 0.390 |
| Kurtosis: | 2.468 | Cond. No. | 191. |
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: | 22:45:54 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.068 |
| Method: | Least Squares | F-statistic: | 0.1113 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.744 |
| Time: | 22:45:54 | Log-Likelihood: | -75.236 |
| 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 | 171.6936 | 234.072 | 0.734 | 0.476 | -333.989 677.376 |
| expression | -9.6358 | 28.879 | -0.334 | 0.744 | -72.026 52.754 |
| Omnibus: | 1.078 | Durbin-Watson: | 1.685 |
| Prob(Omnibus): | 0.583 | Jarque-Bera (JB): | 0.753 |
| Skew: | 0.134 | Prob(JB): | 0.686 |
| Kurtosis: | 1.936 | Cond. No. | 190. |