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 |
| 0.013 | 0.909 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 13.93 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.88e-05 |
| Time: | 22:50:42 | Log-Likelihood: | -99.733 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -32.2360 | 109.275 | -0.295 | 0.771 | -260.951 196.479 |
| C(dose)[T.1] | 342.7123 | 190.185 | 1.802 | 0.087 | -55.350 740.775 |
| expression | 13.1253 | 16.568 | 0.792 | 0.438 | -21.552 47.802 |
| expression:C(dose)[T.1] | -43.1471 | 28.354 | -1.522 | 0.145 | -102.493 16.199 |
| Omnibus: | 0.336 | Durbin-Watson: | 1.977 |
| Prob(Omnibus): | 0.845 | Jarque-Bera (JB): | 0.455 |
| Skew: | 0.235 | Prob(JB): | 0.796 |
| Kurtosis: | 2.496 | Cond. No. | 372. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.81e-05 |
| Time: | 22:50:42 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.7858 | 91.618 | 0.707 | 0.488 | -126.326 255.898 |
| C(dose)[T.1] | 53.6156 | 9.091 | 5.897 | 0.000 | 34.652 72.580 |
| expression | -1.6060 | 13.880 | -0.116 | 0.909 | -30.560 27.348 |
| Omnibus: | 0.368 | Durbin-Watson: | 1.858 |
| Prob(Omnibus): | 0.832 | Jarque-Bera (JB): | 0.512 |
| Skew: | 0.067 | Prob(JB): | 0.774 |
| Kurtosis: | 2.281 | Cond. No. | 143. |
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: | 22:50:42 | 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: | 0.039 |
| Model: | OLS | Adj. R-squared: | -0.006 |
| Method: | Least Squares | F-statistic: | 0.8615 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.364 |
| Time: | 22:50:42 | Log-Likelihood: | -112.64 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -54.0976 | 144.347 | -0.375 | 0.712 | -354.284 246.089 |
| expression | 20.0652 | 21.619 | 0.928 | 0.364 | -24.893 65.023 |
| Omnibus: | 2.601 | Durbin-Watson: | 2.552 |
| Prob(Omnibus): | 0.272 | Jarque-Bera (JB): | 1.596 |
| Skew: | 0.393 | Prob(JB): | 0.450 |
| Kurtosis: | 1.976 | Cond. No. | 139. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.413 | 0.533 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.326 |
| Method: | Least Squares | F-statistic: | 3.257 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0634 |
| Time: | 22:50:42 | Log-Likelihood: | -70.532 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 249.3837 | 432.749 | 0.576 | 0.576 | -703.090 1201.858 |
| C(dose)[T.1] | -68.9635 | 449.792 | -0.153 | 0.881 | -1058.950 921.023 |
| expression | -28.6813 | 68.188 | -0.421 | 0.682 | -178.763 121.400 |
| expression:C(dose)[T.1] | 18.6314 | 70.851 | 0.263 | 0.797 | -137.311 174.574 |
| Omnibus: | 4.988 | Durbin-Watson: | 0.676 |
| Prob(Omnibus): | 0.083 | Jarque-Bera (JB): | 2.868 |
| Skew: | -1.062 | Prob(JB): | 0.238 |
| Kurtosis: | 3.274 | Cond. No. | 583. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.259 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0229 |
| Time: | 22:50:43 | Log-Likelihood: | -70.579 |
| 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 | 139.9039 | 113.403 | 1.234 | 0.241 | -107.180 386.987 |
| C(dose)[T.1] | 49.2399 | 15.476 | 3.182 | 0.008 | 15.521 82.959 |
| expression | -11.4242 | 17.786 | -0.642 | 0.533 | -50.178 27.329 |
| Omnibus: | 5.768 | Durbin-Watson: | 0.733 |
| Prob(Omnibus): | 0.056 | Jarque-Bera (JB): | 3.310 |
| Skew: | -1.130 | Prob(JB): | 0.191 |
| Kurtosis: | 3.438 | Cond. No. | 96.0 |
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: | 22:50:43 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.058 |
| Method: | Least Squares | F-statistic: | 0.2320 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.638 |
| Time: | 22:50:43 | Log-Likelihood: | -75.167 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 164.5934 | 147.590 | 1.115 | 0.285 | -154.255 483.442 |
| expression | -11.1765 | 23.203 | -0.482 | 0.638 | -61.303 38.950 |
| Omnibus: | 0.942 | Durbin-Watson: | 1.659 |
| Prob(Omnibus): | 0.625 | Jarque-Bera (JB): | 0.695 |
| Skew: | -0.061 | Prob(JB): | 0.707 |
| Kurtosis: | 1.953 | Cond. No. | 95.5 |