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 |
| 0.421 | 0.524 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.98 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 7.61e-05 |
| Time: | 20:00:35 | Log-Likelihood: | -100.28 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.5624 | 150.347 | 0.316 | 0.755 | -267.117 362.241 |
| C(dose)[T.1] | 302.5509 | 258.562 | 1.170 | 0.256 | -238.626 843.728 |
| expression | 0.6957 | 15.726 | 0.044 | 0.965 | -32.220 33.611 |
| expression:C(dose)[T.1] | -25.3752 | 26.551 | -0.956 | 0.351 | -80.947 30.197 |
| Omnibus: | 1.466 | Durbin-Watson: | 1.829 |
| Prob(Omnibus): | 0.480 | Jarque-Bera (JB): | 0.958 |
| Skew: | 0.127 | Prob(JB): | 0.619 |
| Kurtosis: | 2.033 | Cond. No. | 710. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.09 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 2.30e-05 |
| Time: | 20:00:35 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.6001 | 120.927 | 1.097 | 0.286 | -119.649 384.849 |
| C(dose)[T.1] | 55.6029 | 9.355 | 5.944 | 0.000 | 36.089 75.116 |
| expression | -8.2063 | 12.643 | -0.649 | 0.524 | -34.580 18.167 |
| Omnibus: | 0.533 | Durbin-Watson: | 1.827 |
| Prob(Omnibus): | 0.766 | Jarque-Bera (JB): | 0.594 |
| Skew: | 0.044 | Prob(JB): | 0.743 |
| Kurtosis: | 2.218 | Cond. No. | 274. |
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, 09 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 20:00:35 | 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: | 0.049 |
| Model: | OLS | Adj. R-squared: | 0.004 |
| Method: | Least Squares | F-statistic: | 1.085 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.309 |
| Time: | 20:00:35 | Log-Likelihood: | -112.53 |
| No. Observations: | 23 | AIC: | 229.1 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -112.4019 | 184.532 | -0.609 | 0.549 | -496.158 271.354 |
| expression | 19.8374 | 19.040 | 1.042 | 0.309 | -19.759 59.434 |
| Omnibus: | 3.085 | Durbin-Watson: | 2.633 |
| Prob(Omnibus): | 0.214 | Jarque-Bera (JB): | 1.599 |
| Skew: | 0.333 | Prob(JB): | 0.450 |
| Kurtosis: | 1.894 | Cond. No. | 257. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.847 | 0.117 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.568 |
| Model: | OLS | Adj. R-squared: | 0.450 |
| Method: | Least Squares | F-statistic: | 4.818 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.0222 |
| Time: | 20:00:35 | Log-Likelihood: | -69.008 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -440.0006 | 492.120 | -0.894 | 0.390 | -1523.150 643.148 |
| C(dose)[T.1] | 362.2383 | 511.358 | 0.708 | 0.493 | -763.254 1487.730 |
| expression | 54.6740 | 53.012 | 1.031 | 0.325 | -62.005 171.353 |
| expression:C(dose)[T.1] | -32.3194 | 55.356 | -0.584 | 0.571 | -154.157 89.518 |
| Omnibus: | 2.183 | Durbin-Watson: | 1.303 |
| Prob(Omnibus): | 0.336 | Jarque-Bera (JB): | 0.631 |
| Skew: | -0.435 | Prob(JB): | 0.729 |
| Kurtosis: | 3.504 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.554 |
| Model: | OLS | Adj. R-squared: | 0.480 |
| Method: | Least Squares | F-statistic: | 7.467 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.00782 |
| Time: | 20:00:35 | Log-Likelihood: | -69.237 |
| No. Observations: | 15 | AIC: | 144.5 |
| Df Residuals: | 12 | BIC: | 146.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -164.9078 | 138.095 | -1.194 | 0.255 | -465.790 135.975 |
| C(dose)[T.1] | 63.8498 | 16.603 | 3.846 | 0.002 | 27.674 100.025 |
| expression | 25.0335 | 14.838 | 1.687 | 0.117 | -7.295 57.362 |
| Omnibus: | 2.460 | Durbin-Watson: | 1.246 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 0.744 |
| Skew: | -0.455 | Prob(JB): | 0.690 |
| Kurtosis: | 3.602 | Cond. No. | 179. |
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, 09 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 20:00:36 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.07033 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.795 |
| Time: | 20:00:36 | Log-Likelihood: | -75.260 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.8442 | 163.125 | 0.839 | 0.417 | -215.565 489.254 |
| expression | -4.8142 | 18.153 | -0.265 | 0.795 | -44.031 34.403 |
| Omnibus: | 0.688 | Durbin-Watson: | 1.547 |
| Prob(Omnibus): | 0.709 | Jarque-Bera (JB): | 0.611 |
| Skew: | 0.047 | Prob(JB): | 0.737 |
| Kurtosis: | 2.015 | Cond. No. | 146. |