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 |
| 0.383 | 0.543 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.26 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000108 |
| Time: | 22:44:35 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 282.4598 | 299.859 | 0.942 | 0.358 | -345.152 910.071 |
| C(dose)[T.1] | -173.4169 | 493.741 | -0.351 | 0.729 | -1206.829 859.995 |
| expression | -22.8058 | 29.954 | -0.761 | 0.456 | -85.501 39.889 |
| expression:C(dose)[T.1] | 22.6567 | 49.216 | 0.460 | 0.650 | -80.353 125.666 |
| Omnibus: | 0.233 | Durbin-Watson: | 1.813 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.429 |
| Skew: | -0.031 | Prob(JB): | 0.807 |
| Kurtosis: | 2.334 | Cond. No. | 1.39e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.04 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.34e-05 |
| Time: | 22:44:35 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 198.4608 | 233.218 | 0.851 | 0.405 | -288.023 684.944 |
| C(dose)[T.1] | 53.8429 | 8.725 | 6.171 | 0.000 | 35.642 72.044 |
| expression | -14.4130 | 23.294 | -0.619 | 0.543 | -63.004 34.178 |
| Omnibus: | 0.387 | Durbin-Watson: | 1.799 |
| Prob(Omnibus): | 0.824 | Jarque-Bera (JB): | 0.519 |
| Skew: | 0.003 | Prob(JB): | 0.771 |
| Kurtosis: | 2.264 | Cond. No. | 545. |
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: | 22:44:35 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0006026 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.981 |
| Time: | 22:44:35 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.2093 | 386.727 | 0.231 | 0.820 | -715.033 893.452 |
| expression | -0.9468 | 38.569 | -0.025 | 0.981 | -81.154 79.261 |
| Omnibus: | 3.310 | Durbin-Watson: | 2.484 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.573 |
| Skew: | 0.291 | Prob(JB): | 0.455 |
| Kurtosis: | 1.858 | Cond. No. | 543. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.236 | 0.636 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.491 |
| Model: | OLS | Adj. R-squared: | 0.352 |
| Method: | Least Squares | F-statistic: | 3.534 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0519 |
| Time: | 22:44:36 | Log-Likelihood: | -70.239 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -343.9530 | 434.588 | -0.791 | 0.445 | -1300.474 612.568 |
| C(dose)[T.1] | 493.5336 | 542.742 | 0.909 | 0.383 | -701.034 1688.101 |
| expression | 43.1581 | 45.577 | 0.947 | 0.364 | -57.155 143.472 |
| expression:C(dose)[T.1] | -46.5532 | 56.549 | -0.823 | 0.428 | -171.017 77.911 |
| Omnibus: | 2.574 | Durbin-Watson: | 1.157 |
| Prob(Omnibus): | 0.276 | Jarque-Bera (JB): | 1.732 |
| Skew: | -0.814 | Prob(JB): | 0.421 |
| Kurtosis: | 2.656 | Cond. No. | 953. |
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: | 5.099 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0250 |
| Time: | 22:44:36 | Log-Likelihood: | -70.687 |
| 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 | -55.7075 | 253.948 | -0.219 | 0.830 | -609.014 497.599 |
| C(dose)[T.1] | 46.9367 | 16.268 | 2.885 | 0.014 | 11.492 82.381 |
| expression | 12.9182 | 26.615 | 0.485 | 0.636 | -45.071 70.907 |
| Omnibus: | 2.548 | Durbin-Watson: | 0.919 |
| Prob(Omnibus): | 0.280 | Jarque-Bera (JB): | 1.836 |
| Skew: | -0.822 | Prob(JB): | 0.399 |
| Kurtosis: | 2.515 | Cond. No. | 318. |
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: | 22:44:36 | 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: | 0.084 |
| Model: | OLS | Adj. R-squared: | 0.014 |
| Method: | Least Squares | F-statistic: | 1.198 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.294 |
| Time: | 22:44:36 | Log-Likelihood: | -74.639 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -242.2029 | 307.073 | -0.789 | 0.444 | -905.594 421.188 |
| expression | 34.8946 | 31.887 | 1.094 | 0.294 | -33.993 103.782 |
| Omnibus: | 1.155 | Durbin-Watson: | 1.824 |
| Prob(Omnibus): | 0.561 | Jarque-Bera (JB): | 0.871 |
| Skew: | 0.304 | Prob(JB): | 0.647 |
| Kurtosis: | 1.989 | Cond. No. | 307. |