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 |
| 4.652 | 0.043 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.743 |
| Model: | OLS | Adj. R-squared: | 0.703 |
| Method: | Least Squares | F-statistic: | 18.35 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 7.72e-06 |
| Time: | 19:51:56 | Log-Likelihood: | -97.459 |
| No. Observations: | 23 | AIC: | 202.9 |
| Df Residuals: | 19 | BIC: | 207.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 6.3687 | 54.955 | 0.116 | 0.909 | -108.653 121.391 |
| C(dose)[T.1] | -95.7374 | 96.325 | -0.994 | 0.333 | -297.348 105.873 |
| expression | 7.9799 | 9.124 | 0.875 | 0.393 | -11.116 27.076 |
| expression:C(dose)[T.1] | 21.6155 | 14.964 | 1.445 | 0.165 | -9.704 52.935 |
| Omnibus: | 2.082 | Durbin-Watson: | 2.099 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.772 |
| Skew: | 0.630 | Prob(JB): | 0.412 |
| Kurtosis: | 2.486 | Cond. No. | 201. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.715 |
| Model: | OLS | Adj. R-squared: | 0.687 |
| Method: | Least Squares | F-statistic: | 25.12 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 3.50e-06 |
| Time: | 19:51:56 | Log-Likelihood: | -98.658 |
| No. Observations: | 23 | AIC: | 203.3 |
| Df Residuals: | 20 | BIC: | 206.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.8055 | 44.850 | -0.932 | 0.362 | -135.360 51.749 |
| C(dose)[T.1] | 42.7900 | 9.290 | 4.606 | 0.000 | 23.411 62.169 |
| expression | 16.0157 | 7.425 | 2.157 | 0.043 | 0.526 31.505 |
| Omnibus: | 0.205 | Durbin-Watson: | 2.192 |
| Prob(Omnibus): | 0.903 | Jarque-Bera (JB): | 0.407 |
| Skew: | 0.095 | Prob(JB): | 0.816 |
| Kurtosis: | 2.377 | Cond. No. | 74.5 |
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: | 19:51:56 | 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: | 0.413 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 14.79 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.000938 |
| Time: | 19:51:56 | Log-Likelihood: | -106.97 |
| No. Observations: | 23 | AIC: | 217.9 |
| Df Residuals: | 21 | BIC: | 220.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -134.9330 | 56.084 | -2.406 | 0.025 | -251.565 -18.301 |
| expression | 34.0178 | 8.845 | 3.846 | 0.001 | 15.624 52.412 |
| Omnibus: | 8.584 | Durbin-Watson: | 2.653 |
| Prob(Omnibus): | 0.014 | Jarque-Bera (JB): | 7.876 |
| Skew: | -0.752 | Prob(JB): | 0.0195 |
| Kurtosis: | 5.440 | Cond. No. | 65.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.419 | 0.530 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.520 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 3.978 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.0382 |
| Time: | 19:51:56 | Log-Likelihood: | -69.789 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.5074 | 111.674 | 0.479 | 0.641 | -192.286 299.301 |
| C(dose)[T.1] | -223.5910 | 251.209 | -0.890 | 0.392 | -776.499 329.317 |
| expression | 2.1797 | 17.397 | 0.125 | 0.903 | -36.111 40.471 |
| expression:C(dose)[T.1] | 45.3805 | 41.146 | 1.103 | 0.294 | -45.181 135.942 |
| Omnibus: | 2.632 | Durbin-Watson: | 1.211 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.825 |
| Skew: | -0.831 | Prob(JB): | 0.402 |
| Kurtosis: | 2.601 | Cond. No. | 245. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.265 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.0228 |
| Time: | 19:51:56 | Log-Likelihood: | -70.576 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.6926 | 102.221 | 0.017 | 0.987 | -221.028 224.413 |
| C(dose)[T.1] | 52.8853 | 16.489 | 3.207 | 0.008 | 16.959 88.812 |
| expression | 10.2926 | 15.907 | 0.647 | 0.530 | -24.366 44.951 |
| Omnibus: | 2.410 | Durbin-Watson: | 0.865 |
| Prob(Omnibus): | 0.300 | Jarque-Bera (JB): | 1.747 |
| Skew: | -0.798 | Prob(JB): | 0.418 |
| Kurtosis: | 2.500 | Cond. No. | 84.9 |
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: | 19:51:56 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.065 |
| Method: | Least Squares | F-statistic: | 0.1413 |
| Date: | Sun, 09 Nov 2025 | Prob (F-statistic): | 0.713 |
| Time: | 19:51:56 | Log-Likelihood: | -75.219 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 139.1881 | 121.503 | 1.146 | 0.273 | -123.304 401.680 |
| expression | -7.3474 | 19.543 | -0.376 | 0.713 | -49.568 34.873 |
| Omnibus: | 0.621 | Durbin-Watson: | 1.516 |
| Prob(Omnibus): | 0.733 | Jarque-Bera (JB): | 0.586 |
| Skew: | 0.028 | Prob(JB): | 0.746 |
| Kurtosis: | 2.033 | Cond. No. | 76.7 |