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 |
| 0.775 | 0.389 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.690 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 14.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.48e-05 |
| Time: | 04:51:36 | Log-Likelihood: | -99.627 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 19 | BIC: | 211.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.8941 | 103.553 | 1.930 | 0.069 | -16.845 416.634 |
| C(dose)[T.1] | -235.2051 | 220.143 | -1.068 | 0.299 | -695.971 225.560 |
| expression | -21.0898 | 14.967 | -1.409 | 0.175 | -52.415 10.236 |
| expression:C(dose)[T.1] | 41.7075 | 31.770 | 1.313 | 0.205 | -24.788 108.203 |
| Omnibus: | 0.201 | Durbin-Watson: | 1.717 |
| Prob(Omnibus): | 0.905 | Jarque-Bera (JB): | 0.315 |
| Skew: | 0.189 | Prob(JB): | 0.854 |
| Kurtosis: | 2.568 | Cond. No. | 430. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.94e-05 |
| Time: | 04:51:36 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 135.9538 | 93.022 | 1.462 | 0.159 | -58.087 329.995 |
| C(dose)[T.1] | 53.5847 | 8.609 | 6.224 | 0.000 | 35.626 71.543 |
| expression | -11.8336 | 13.438 | -0.881 | 0.389 | -39.866 16.199 |
| Omnibus: | 0.187 | Durbin-Watson: | 1.676 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.384 |
| Skew: | 0.128 | Prob(JB): | 0.825 |
| Kurtosis: | 2.421 | Cond. No. | 153. |
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: | 04:51:36 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.039 |
| Method: | Least Squares | F-statistic: | 0.1642 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.689 |
| Time: | 04:51:36 | Log-Likelihood: | -113.02 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.6853 | 155.565 | 0.917 | 0.369 | -180.830 466.201 |
| expression | -9.1022 | 22.463 | -0.405 | 0.689 | -55.817 37.613 |
| Omnibus: | 3.926 | Durbin-Watson: | 2.470 |
| Prob(Omnibus): | 0.140 | Jarque-Bera (JB): | 1.861 |
| Skew: | 0.380 | Prob(JB): | 0.394 |
| Kurtosis: | 1.832 | Cond. No. | 153. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.803 | 0.388 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.499 |
| Model: | OLS | Adj. R-squared: | 0.363 |
| Method: | Least Squares | F-statistic: | 3.659 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0475 |
| Time: | 04:51:36 | Log-Likelihood: | -70.109 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -158.4416 | 215.049 | -0.737 | 0.477 | -631.761 314.877 |
| C(dose)[T.1] | 250.7243 | 340.768 | 0.736 | 0.477 | -499.301 1000.749 |
| expression | 30.7854 | 29.269 | 1.052 | 0.315 | -33.635 95.206 |
| expression:C(dose)[T.1] | -27.4952 | 46.164 | -0.596 | 0.563 | -129.102 74.111 |
| Omnibus: | 2.136 | Durbin-Watson: | 0.783 |
| Prob(Omnibus): | 0.344 | Jarque-Bera (JB): | 1.509 |
| Skew: | -0.744 | Prob(JB): | 0.470 |
| Kurtosis: | 2.556 | Cond. No. | 417. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.483 |
| Model: | OLS | Adj. R-squared: | 0.397 |
| Method: | Least Squares | F-statistic: | 5.613 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0190 |
| Time: | 04:51:36 | Log-Likelihood: | -70.347 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -77.3496 | 161.922 | -0.478 | 0.641 | -430.147 275.447 |
| C(dose)[T.1] | 47.9798 | 15.298 | 3.136 | 0.009 | 14.648 81.312 |
| expression | 19.7328 | 22.017 | 0.896 | 0.388 | -28.239 67.704 |
| Omnibus: | 2.085 | Durbin-Watson: | 0.648 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.582 |
| Skew: | -0.735 | Prob(JB): | 0.453 |
| Kurtosis: | 2.393 | Cond. No. | 160. |
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: | 04:51:36 | 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: | 0.060 |
| Model: | OLS | Adj. R-squared: | -0.012 |
| Method: | Least Squares | F-statistic: | 0.8278 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.379 |
| Time: | 04:51:36 | Log-Likelihood: | -74.837 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -96.9179 | 209.701 | -0.462 | 0.652 | -549.949 356.113 |
| expression | 25.8602 | 28.423 | 0.910 | 0.379 | -35.543 87.263 |
| Omnibus: | 3.273 | Durbin-Watson: | 1.577 |
| Prob(Omnibus): | 0.195 | Jarque-Bera (JB): | 1.317 |
| Skew: | 0.286 | Prob(JB): | 0.518 |
| Kurtosis: | 1.666 | Cond. No. | 160. |