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 |
| 1.205 | 0.285 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 13.05 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 7.36e-05 |
| Time: | 22:00:09 | Log-Likelihood: | -100.24 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.2870 | 255.808 | 0.435 | 0.668 | -424.125 646.699 |
| C(dose)[T.1] | 212.6703 | 316.275 | 0.672 | 0.509 | -449.300 874.641 |
| expression | -6.1297 | 27.464 | -0.223 | 0.826 | -63.612 51.352 |
| expression:C(dose)[T.1] | -16.7277 | 33.759 | -0.495 | 0.626 | -87.387 53.932 |
| Omnibus: | 1.214 | Durbin-Watson: | 1.833 |
| Prob(Omnibus): | 0.545 | Jarque-Bera (JB): | 0.930 |
| Skew: | 0.204 | Prob(JB): | 0.628 |
| Kurtosis: | 2.103 | Cond. No. | 966. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.21 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 1.58e-05 |
| Time: | 22:00:09 | Log-Likelihood: | -100.39 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 214.3724 | 146.012 | 1.468 | 0.158 | -90.203 518.948 |
| C(dose)[T.1] | 56.0210 | 8.861 | 6.322 | 0.000 | 37.537 74.505 |
| expression | -17.2000 | 15.667 | -1.098 | 0.285 | -49.882 15.482 |
| Omnibus: | 1.486 | Durbin-Watson: | 1.872 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 0.949 |
| Skew: | 0.095 | Prob(JB): | 0.622 |
| Kurtosis: | 2.024 | Cond. No. | 326. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:00:09 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.1584 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.695 |
| Time: | 22:00:09 | 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 | -15.3611 | 238.982 | -0.064 | 0.949 | -512.352 481.630 |
| expression | 10.1293 | 25.449 | 0.398 | 0.695 | -42.794 63.053 |
| Omnibus: | 3.838 | Durbin-Watson: | 2.436 |
| Prob(Omnibus): | 0.147 | Jarque-Bera (JB): | 1.741 |
| Skew: | 0.329 | Prob(JB): | 0.419 |
| Kurtosis: | 1.824 | Cond. No. | 316. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.225 | 0.644 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.605 |
| Model: | OLS | Adj. R-squared: | 0.498 |
| Method: | Least Squares | F-statistic: | 5.628 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0138 |
| Time: | 22:00:09 | Log-Likelihood: | -68.324 |
| No. Observations: | 15 | AIC: | 144.6 |
| Df Residuals: | 11 | BIC: | 147.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.0651 | 219.072 | 1.068 | 0.308 | -248.108 716.238 |
| C(dose)[T.1] | -698.2558 | 370.254 | -1.886 | 0.086 | -1513.179 116.667 |
| expression | -18.6275 | 24.463 | -0.761 | 0.462 | -72.469 35.214 |
| expression:C(dose)[T.1] | 83.9158 | 41.510 | 2.022 | 0.068 | -7.446 175.278 |
| Omnibus: | 1.101 | Durbin-Watson: | 1.366 |
| Prob(Omnibus): | 0.577 | Jarque-Bera (JB): | 0.736 |
| Skew: | -0.021 | Prob(JB): | 0.692 |
| Kurtosis: | 1.916 | Cond. No. | 601. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.089 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0251 |
| Time: | 22:00:09 | Log-Likelihood: | -70.694 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -26.6508 | 198.563 | -0.134 | 0.895 | -459.283 405.982 |
| C(dose)[T.1] | 49.7178 | 15.633 | 3.180 | 0.008 | 15.657 83.778 |
| expression | 10.5167 | 22.160 | 0.475 | 0.644 | -37.766 58.799 |
| Omnibus: | 2.266 | Durbin-Watson: | 0.830 |
| Prob(Omnibus): | 0.322 | Jarque-Bera (JB): | 1.670 |
| Skew: | -0.772 | Prob(JB): | 0.434 |
| Kurtosis: | 2.463 | Cond. No. | 231. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:00:09 | 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: | 0.03725 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.850 |
| Time: | 22:00:09 | Log-Likelihood: | -75.279 |
| 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 | 44.0370 | 257.354 | 0.171 | 0.867 | -511.943 600.017 |
| expression | 5.5643 | 28.831 | 0.193 | 0.850 | -56.722 67.850 |
| Omnibus: | 0.318 | Durbin-Watson: | 1.656 |
| Prob(Omnibus): | 0.853 | Jarque-Bera (JB): | 0.459 |
| Skew: | -0.005 | Prob(JB): | 0.795 |
| Kurtosis: | 2.143 | Cond. No. | 229. |