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 |
| 0.118 | 0.734 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.600 |
| Method: | Least Squares | F-statistic: | 12.01 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.000123 |
| Time: | 19:18:56 | Log-Likelihood: | -100.88 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.1764 | 178.850 | -0.230 | 0.820 | -415.514 333.161 |
| C(dose)[T.1] | 188.9578 | 306.015 | 0.617 | 0.544 | -451.539 829.455 |
| expression | 12.2588 | 22.972 | 0.534 | 0.600 | -35.822 60.340 |
| expression:C(dose)[T.1] | -17.3974 | 39.150 | -0.444 | 0.662 | -99.339 64.544 |
| Omnibus: | 0.756 | Durbin-Watson: | 1.848 |
| Prob(Omnibus): | 0.685 | Jarque-Bera (JB): | 0.700 |
| Skew: | -0.094 | Prob(JB): | 0.705 |
| Kurtosis: | 2.166 | Cond. No. | 661. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.66 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.67e-05 |
| Time: | 19:18:56 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.4308 | 141.934 | 0.038 | 0.970 | -290.637 301.499 |
| C(dose)[T.1] | 53.0293 | 8.790 | 6.033 | 0.000 | 34.694 71.364 |
| expression | 6.2688 | 18.225 | 0.344 | 0.734 | -31.747 44.285 |
| Omnibus: | 0.380 | Durbin-Watson: | 1.907 |
| Prob(Omnibus): | 0.827 | Jarque-Bera (JB): | 0.519 |
| Skew: | 0.071 | Prob(JB): | 0.771 |
| Kurtosis: | 2.278 | Cond. No. | 258. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 19:18:56 | 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: | 0.016 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.3455 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.563 |
| Time: | 19:18:56 | Log-Likelihood: | -112.92 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.5745 | 231.989 | -0.244 | 0.810 | -539.022 425.873 |
| expression | 17.4634 | 29.711 | 0.588 | 0.563 | -44.324 79.251 |
| Omnibus: | 1.559 | Durbin-Watson: | 2.616 |
| Prob(Omnibus): | 0.459 | Jarque-Bera (JB): | 1.191 |
| Skew: | 0.338 | Prob(JB): | 0.551 |
| Kurtosis: | 2.113 | Cond. No. | 257. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.139 | 0.716 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.522 |
| Model: | OLS | Adj. R-squared: | 0.392 |
| Method: | Least Squares | F-statistic: | 4.010 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0374 |
| Time: | 19:18:56 | Log-Likelihood: | -69.759 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -350.6286 | 333.019 | -1.053 | 0.315 | -1083.600 382.342 |
| C(dose)[T.1] | 537.6350 | 391.871 | 1.372 | 0.197 | -324.868 1400.138 |
| expression | 52.5484 | 41.836 | 1.256 | 0.235 | -39.532 144.629 |
| expression:C(dose)[T.1] | -61.5465 | 49.454 | -1.245 | 0.239 | -170.395 47.302 |
| Omnibus: | 2.415 | Durbin-Watson: | 1.068 |
| Prob(Omnibus): | 0.299 | Jarque-Bera (JB): | 1.380 |
| Skew: | -0.741 | Prob(JB): | 0.502 |
| Kurtosis: | 2.886 | Cond. No. | 602. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.011 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0262 |
| Time: | 19:18:56 | Log-Likelihood: | -70.747 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -0.2274 | 181.861 | -0.001 | 0.999 | -396.468 396.014 |
| C(dose)[T.1] | 50.3345 | 15.944 | 3.157 | 0.008 | 15.595 85.074 |
| expression | 8.5041 | 22.814 | 0.373 | 0.716 | -41.204 58.212 |
| Omnibus: | 2.177 | Durbin-Watson: | 0.933 |
| Prob(Omnibus): | 0.337 | Jarque-Bera (JB): | 1.531 |
| Skew: | -0.752 | Prob(JB): | 0.465 |
| Kurtosis: | 2.564 | Cond. No. | 187. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 19:18:56 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03300 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.859 |
| Time: | 19:18:56 | Log-Likelihood: | -75.281 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.3544 | 229.711 | 0.589 | 0.566 | -360.905 631.614 |
| expression | -5.2874 | 29.107 | -0.182 | 0.859 | -68.169 57.594 |
| Omnibus: | 0.355 | Durbin-Watson: | 1.563 |
| Prob(Omnibus): | 0.837 | Jarque-Bera (JB): | 0.478 |
| Skew: | -0.022 | Prob(JB): | 0.788 |
| Kurtosis: | 2.127 | Cond. No. | 181. |