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 |
| 1.653 | 0.213 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 13.80 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.17e-05 |
| Time: | 23:04:45 | Log-Likelihood: | -99.804 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -58.7243 | 672.337 | -0.087 | 0.931 | -1465.942 1348.493 |
| C(dose)[T.1] | -591.0848 | 846.444 | -0.698 | 0.493 | -2362.712 1180.542 |
| expression | 10.6139 | 63.187 | 0.168 | 0.868 | -121.637 142.865 |
| expression:C(dose)[T.1] | 60.5294 | 79.533 | 0.761 | 0.456 | -105.936 226.995 |
| Omnibus: | 0.342 | Durbin-Watson: | 1.868 |
| Prob(Omnibus): | 0.843 | Jarque-Bera (JB): | 0.496 |
| Skew: | 0.051 | Prob(JB): | 0.780 |
| Kurtosis: | 2.288 | Cond. No. | 2.94e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 20.85 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.28e-05 |
| Time: | 23:04:45 | Log-Likelihood: | -100.15 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -465.2262 | 404.025 | -1.151 | 0.263 | -1308.007 377.554 |
| C(dose)[T.1] | 53.0744 | 8.431 | 6.295 | 0.000 | 35.488 70.661 |
| expression | 48.8186 | 37.968 | 1.286 | 0.213 | -30.381 128.018 |
| Omnibus: | 1.198 | Durbin-Watson: | 2.013 |
| Prob(Omnibus): | 0.549 | Jarque-Bera (JB): | 0.859 |
| Skew: | 0.091 | Prob(JB): | 0.651 |
| Kurtosis: | 2.071 | Cond. No. | 1.03e+03 |
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: | 23:04:45 | 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: | 0.034 |
| Model: | OLS | Adj. R-squared: | -0.012 |
| Method: | Least Squares | F-statistic: | 0.7290 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.403 |
| Time: | 23:04:45 | Log-Likelihood: | -112.71 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -501.4839 | 680.748 | -0.737 | 0.469 | -1917.177 914.210 |
| expression | 54.6105 | 63.961 | 0.854 | 0.403 | -78.403 187.624 |
| Omnibus: | 4.588 | Durbin-Watson: | 2.658 |
| Prob(Omnibus): | 0.101 | Jarque-Bera (JB): | 1.665 |
| Skew: | 0.199 | Prob(JB): | 0.435 |
| Kurtosis: | 1.743 | Cond. No. | 1.03e+03 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.284 | 0.157 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.545 |
| Model: | OLS | Adj. R-squared: | 0.421 |
| Method: | Least Squares | F-statistic: | 4.386 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0292 |
| Time: | 23:04:45 | Log-Likelihood: | -69.399 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -870.4204 | 752.821 | -1.156 | 0.272 | -2527.369 786.528 |
| C(dose)[T.1] | 464.4790 | 961.200 | 0.483 | 0.638 | -1651.107 2580.065 |
| expression | 85.3408 | 68.497 | 1.246 | 0.239 | -65.420 236.101 |
| expression:C(dose)[T.1] | -37.8826 | 87.389 | -0.433 | 0.673 | -230.224 154.459 |
| Omnibus: | 2.317 | Durbin-Watson: | 1.070 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.712 |
| Skew: | -0.684 | Prob(JB): | 0.425 |
| Kurtosis: | 2.068 | Cond. No. | 2.00e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.537 |
| Model: | OLS | Adj. R-squared: | 0.460 |
| Method: | Least Squares | F-statistic: | 6.956 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00986 |
| Time: | 23:04:45 | Log-Likelihood: | -69.526 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 12 | BIC: | 147.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -614.6532 | 451.476 | -1.361 | 0.198 | -1598.334 369.028 |
| C(dose)[T.1] | 47.8546 | 14.454 | 3.311 | 0.006 | 16.362 79.347 |
| expression | 62.0670 | 41.071 | 1.511 | 0.157 | -27.420 151.554 |
| Omnibus: | 2.540 | Durbin-Watson: | 0.935 |
| Prob(Omnibus): | 0.281 | Jarque-Bera (JB): | 1.942 |
| Skew: | -0.793 | Prob(JB): | 0.379 |
| Kurtosis: | 2.231 | Cond. No. | 697. |
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: | 23:04:45 | 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: | 0.114 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 1.671 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.219 |
| Time: | 23:04:45 | Log-Likelihood: | -74.393 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -681.0281 | 599.426 | -1.136 | 0.276 | -1976.009 613.953 |
| expression | 70.4205 | 54.481 | 1.293 | 0.219 | -47.280 188.121 |
| Omnibus: | 2.819 | Durbin-Watson: | 1.800 |
| Prob(Omnibus): | 0.244 | Jarque-Bera (JB): | 1.202 |
| Skew: | 0.246 | Prob(JB): | 0.548 |
| Kurtosis: | 1.703 | Cond. No. | 695. |