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 |
| 4.039 | 0.058 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.728 |
| Model: | OLS | Adj. R-squared: | 0.685 |
| Method: | Least Squares | F-statistic: | 16.95 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 1.33e-05 |
| Time: | 08:31:36 | Log-Likelihood: | -98.131 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 19 | BIC: | 208.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.7197 | 44.382 | 1.503 | 0.149 | -26.174 159.613 |
| C(dose)[T.1] | 119.3556 | 56.009 | 2.131 | 0.046 | 2.127 236.584 |
| expression | -5.2686 | 18.547 | -0.284 | 0.779 | -44.087 33.550 |
| expression:C(dose)[T.1] | -27.5167 | 23.275 | -1.182 | 0.252 | -76.231 21.198 |
| Omnibus: | 0.441 | Durbin-Watson: | 2.120 |
| Prob(Omnibus): | 0.802 | Jarque-Bera (JB): | 0.572 |
| Skew: | 0.214 | Prob(JB): | 0.751 |
| Kurtosis: | 2.357 | Cond. No. | 55.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.708 |
| Model: | OLS | Adj. R-squared: | 0.679 |
| Method: | Least Squares | F-statistic: | 24.25 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 4.50e-06 |
| Time: | 08:31:36 | Log-Likelihood: | -98.948 |
| No. Observations: | 23 | AIC: | 203.9 |
| Df Residuals: | 20 | BIC: | 207.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 108.2123 | 27.436 | 3.944 | 0.001 | 50.982 165.443 |
| C(dose)[T.1] | 53.8049 | 8.003 | 6.723 | 0.000 | 37.112 70.498 |
| expression | -22.7413 | 11.316 | -2.010 | 0.058 | -46.346 0.864 |
| Omnibus: | 0.958 | Durbin-Watson: | 1.954 |
| Prob(Omnibus): | 0.619 | Jarque-Bera (JB): | 0.790 |
| Skew: | 0.124 | Prob(JB): | 0.674 |
| Kurtosis: | 2.127 | Cond. No. | 19.6 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 08:31:36 | 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: | 0.048 |
| Model: | OLS | Adj. R-squared: | 0.003 |
| Method: | Least Squares | F-statistic: | 1.061 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.315 |
| Time: | 08:31:36 | Log-Likelihood: | -112.54 |
| 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 | 128.6684 | 48.046 | 2.678 | 0.014 | 28.751 228.586 |
| expression | -20.5284 | 19.931 | -1.030 | 0.315 | -61.978 20.921 |
| Omnibus: | 2.228 | Durbin-Watson: | 2.520 |
| Prob(Omnibus): | 0.328 | Jarque-Bera (JB): | 1.156 |
| Skew: | 0.124 | Prob(JB): | 0.561 |
| Kurtosis: | 1.930 | Cond. No. | 19.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.467 | 0.142 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.546 |
| Model: | OLS | Adj. R-squared: | 0.422 |
| Method: | Least Squares | F-statistic: | 4.412 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0287 |
| Time: | 08:31:36 | Log-Likelihood: | -69.376 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.6165 | 49.202 | 0.683 | 0.509 | -74.677 141.910 |
| C(dose)[T.1] | 9.5316 | 73.641 | 0.129 | 0.899 | -152.551 171.614 |
| expression | 10.8865 | 15.448 | 0.705 | 0.496 | -23.115 44.888 |
| expression:C(dose)[T.1] | 5.5759 | 19.599 | 0.284 | 0.781 | -37.562 48.714 |
| Omnibus: | 2.138 | Durbin-Watson: | 0.984 |
| Prob(Omnibus): | 0.343 | Jarque-Bera (JB): | 1.110 |
| Skew: | -0.288 | Prob(JB): | 0.574 |
| Kurtosis: | 1.798 | Cond. No. | 58.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.543 |
| Model: | OLS | Adj. R-squared: | 0.467 |
| Method: | Least Squares | F-statistic: | 7.123 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00914 |
| Time: | 08:31:37 | Log-Likelihood: | -69.431 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 12 | BIC: | 147.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.8573 | 30.244 | 0.756 | 0.464 | -43.039 88.754 |
| C(dose)[T.1] | 29.7161 | 18.955 | 1.568 | 0.143 | -11.583 71.015 |
| expression | 14.3506 | 9.136 | 1.571 | 0.142 | -5.555 34.256 |
| Omnibus: | 2.457 | Durbin-Watson: | 0.966 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.167 |
| Skew: | -0.280 | Prob(JB): | 0.558 |
| Kurtosis: | 1.753 | Cond. No. | 19.0 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 08:31:37 | 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: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 10.60 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00626 |
| Time: | 08:31:37 | Log-Likelihood: | -70.828 |
| 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 | 2.8170 | 28.906 | 0.097 | 0.924 | -59.631 65.265 |
| expression | 23.7214 | 7.286 | 3.256 | 0.006 | 7.981 39.462 |
| Omnibus: | 2.732 | Durbin-Watson: | 1.359 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.229 |
| Skew: | 0.290 | Prob(JB): | 0.541 |
| Kurtosis: | 1.724 | Cond. No. | 16.1 |