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 |
| 2.090 | 0.164 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.732 |
| Model: | OLS | Adj. R-squared: | 0.690 |
| Method: | Least Squares | F-statistic: | 17.33 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 1.15e-05 |
| Time: | 18:22:42 | Log-Likelihood: | -97.949 |
| No. Observations: | 23 | AIC: | 203.9 |
| Df Residuals: | 19 | BIC: | 208.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -22.7491 | 207.284 | -0.110 | 0.914 | -456.599 411.101 |
| C(dose)[T.1] | 586.4920 | 281.180 | 2.086 | 0.051 | -2.025 1175.009 |
| expression | 8.2010 | 22.082 | 0.371 | 0.714 | -38.016 54.418 |
| expression:C(dose)[T.1] | -56.0365 | 29.734 | -1.885 | 0.075 | -118.271 6.198 |
| Omnibus: | 1.267 | Durbin-Watson: | 2.295 |
| Prob(Omnibus): | 0.531 | Jarque-Bera (JB): | 0.978 |
| Skew: | 0.483 | Prob(JB): | 0.613 |
| Kurtosis: | 2.701 | Cond. No. | 909. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.650 |
| Method: | Least Squares | F-statistic: | 21.47 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 1.05e-05 |
| Time: | 18:22:42 | Log-Likelihood: | -99.920 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 20 | BIC: | 209.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 267.2572 | 147.469 | 1.812 | 0.085 | -40.358 574.872 |
| C(dose)[T.1] | 56.8075 | 8.683 | 6.542 | 0.000 | 38.695 74.920 |
| expression | -22.7036 | 15.703 | -1.446 | 0.164 | -55.459 10.052 |
| Omnibus: | 3.561 | Durbin-Watson: | 2.288 |
| Prob(Omnibus): | 0.169 | Jarque-Bera (JB): | 1.422 |
| Skew: | 0.117 | Prob(JB): | 0.491 |
| Kurtosis: | 1.805 | Cond. No. | 339. |
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: | Sun, 21 Dec 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 18:22:42 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.04764 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 0.829 |
| Time: | 18:22:42 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.8473 | 246.912 | 0.105 | 0.918 | -487.634 539.329 |
| expression | 5.6963 | 26.098 | 0.218 | 0.829 | -48.577 59.969 |
| Omnibus: | 3.137 | Durbin-Watson: | 2.426 |
| Prob(Omnibus): | 0.208 | Jarque-Bera (JB): | 1.620 |
| Skew: | 0.339 | Prob(JB): | 0.445 |
| Kurtosis: | 1.890 | Cond. No. | 328. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.160 | 0.696 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.309 |
| Method: | Least Squares | F-statistic: | 3.089 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 0.0719 |
| Time: | 18:22:42 | Log-Likelihood: | -70.717 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -203.0181 | 958.191 | -0.212 | 0.836 | -2311.983 1905.947 |
| C(dose)[T.1] | 206.9683 | 1028.087 | 0.201 | 0.844 | -2055.836 2469.773 |
| expression | 28.1561 | 99.749 | 0.282 | 0.783 | -191.390 247.702 |
| expression:C(dose)[T.1] | -16.6054 | 106.806 | -0.155 | 0.879 | -251.684 218.473 |
| Omnibus: | 2.452 | Durbin-Watson: | 0.939 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.663 |
| Skew: | -0.795 | Prob(JB): | 0.435 |
| Kurtosis: | 2.636 | Cond. No. | 1.94e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.030 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 0.0259 |
| Time: | 18:22:42 | Log-Likelihood: | -70.734 |
| 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 | -63.8992 | 328.470 | -0.195 | 0.849 | -779.574 651.776 |
| C(dose)[T.1] | 47.1509 | 16.451 | 2.866 | 0.014 | 11.308 82.993 |
| expression | 13.6725 | 34.176 | 0.400 | 0.696 | -60.791 88.136 |
| Omnibus: | 2.551 | Durbin-Watson: | 0.910 |
| Prob(Omnibus): | 0.279 | Jarque-Bera (JB): | 1.717 |
| Skew: | -0.811 | Prob(JB): | 0.424 |
| Kurtosis: | 2.655 | Cond. No. | 413. |
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: | Sun, 21 Dec 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 18:22:42 | 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: | 0.013 |
| Method: | Least Squares | F-statistic: | 1.186 |
| Date: | Sun, 21 Dec 2025 | Prob (F-statistic): | 0.296 |
| Time: | 18:22:42 | Log-Likelihood: | -74.645 |
| 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 | -333.6269 | 392.434 | -0.850 | 0.411 | -1181.429 514.175 |
| expression | 44.1188 | 40.507 | 1.089 | 0.296 | -43.391 131.629 |
| Omnibus: | 0.418 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.812 | Jarque-Bera (JB): | 0.521 |
| Skew: | 0.160 | Prob(JB): | 0.771 |
| Kurtosis: | 2.145 | Cond. No. | 395. |