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 |
| 0.027 | 0.872 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.595 |
| Method: | Least Squares | F-statistic: | 11.79 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000137 |
| Time: | 04:53:07 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.2872 | 80.797 | 0.622 | 0.541 | -118.822 219.397 |
| C(dose)[T.1] | 5.6279 | 200.439 | 0.028 | 0.978 | -413.895 425.151 |
| expression | 0.5169 | 10.619 | 0.049 | 0.962 | -21.709 22.743 |
| expression:C(dose)[T.1] | 6.1447 | 25.927 | 0.237 | 0.815 | -48.121 60.411 |
| Omnibus: | 0.431 | Durbin-Watson: | 1.897 |
| Prob(Omnibus): | 0.806 | Jarque-Bera (JB): | 0.550 |
| Skew: | 0.091 | Prob(JB): | 0.760 |
| Kurtosis: | 2.265 | Cond. No. | 403. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 04:53:07 | Log-Likelihood: | -101.05 |
| 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 | 42.4674 | 71.991 | 0.590 | 0.562 | -107.704 192.639 |
| C(dose)[T.1] | 53.0828 | 8.901 | 5.964 | 0.000 | 34.516 71.649 |
| expression | 1.5477 | 9.456 | 0.164 | 0.872 | -18.178 21.273 |
| Omnibus: | 0.349 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.501 |
| Skew: | 0.060 | Prob(JB): | 0.778 |
| Kurtosis: | 2.287 | Cond. No. | 129. |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:53:07 | 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: | 0.026 |
| Model: | OLS | Adj. R-squared: | -0.020 |
| Method: | Least Squares | F-statistic: | 0.5660 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.460 |
| Time: | 04:53:07 | Log-Likelihood: | -112.80 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -7.6173 | 116.308 | -0.065 | 0.948 | -249.492 234.258 |
| expression | 11.3944 | 15.146 | 0.752 | 0.460 | -20.103 42.892 |
| Omnibus: | 2.403 | Durbin-Watson: | 2.573 |
| Prob(Omnibus): | 0.301 | Jarque-Bera (JB): | 1.312 |
| Skew: | 0.246 | Prob(JB): | 0.519 |
| Kurtosis: | 1.938 | Cond. No. | 128. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.317 | 0.584 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 3.177 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0673 |
| Time: | 04:53:07 | Log-Likelihood: | -70.620 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.8369 | 139.268 | 1.040 | 0.321 | -161.690 451.364 |
| C(dose)[T.1] | -4.1275 | 340.638 | -0.012 | 0.991 | -753.866 745.611 |
| expression | -9.7337 | 17.449 | -0.558 | 0.588 | -48.139 28.671 |
| expression:C(dose)[T.1] | 6.7754 | 41.960 | 0.161 | 0.875 | -85.577 99.128 |
| Omnibus: | 2.293 | Durbin-Watson: | 0.971 |
| Prob(Omnibus): | 0.318 | Jarque-Bera (JB): | 1.620 |
| Skew: | -0.774 | Prob(JB): | 0.445 |
| Kurtosis: | 2.556 | Cond. No. | 409. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.172 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0240 |
| Time: | 04:53:07 | Log-Likelihood: | -70.638 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.5190 | 121.498 | 1.115 | 0.287 | -129.203 400.241 |
| C(dose)[T.1] | 50.8124 | 15.799 | 3.216 | 0.007 | 16.390 85.235 |
| expression | -8.5620 | 15.211 | -0.563 | 0.584 | -41.704 24.580 |
| Omnibus: | 2.695 | Durbin-Watson: | 0.948 |
| Prob(Omnibus): | 0.260 | Jarque-Bera (JB): | 1.824 |
| Skew: | -0.836 | Prob(JB): | 0.402 |
| Kurtosis: | 2.651 | Cond. No. | 129. |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:53:07 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.0002795 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.987 |
| Time: | 04:53:07 | Log-Likelihood: | -75.300 |
| 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 | 91.0264 | 158.250 | 0.575 | 0.575 | -250.853 432.905 |
| expression | 0.3279 | 19.610 | 0.017 | 0.987 | -42.037 42.693 |
| Omnibus: | 0.545 | Durbin-Watson: | 1.618 |
| Prob(Omnibus): | 0.761 | Jarque-Bera (JB): | 0.559 |
| Skew: | 0.042 | Prob(JB): | 0.756 |
| Kurtosis: | 2.058 | Cond. No. | 128. |