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 |
| 0.236 | 0.632 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.95 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.000126 |
| Time: | 08:55:23 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -48.7730 | 389.866 | -0.125 | 0.902 | -864.772 767.226 |
| C(dose)[T.1] | -46.0086 | 614.733 | -0.075 | 0.941 | -1332.659 1240.642 |
| expression | 9.3767 | 35.494 | 0.264 | 0.794 | -64.913 83.666 |
| expression:C(dose)[T.1] | 9.0830 | 56.035 | 0.162 | 0.873 | -108.200 126.365 |
| Omnibus: | 0.741 | Durbin-Watson: | 2.036 |
| Prob(Omnibus): | 0.691 | Jarque-Bera (JB): | 0.701 |
| Skew: | 0.116 | Prob(JB): | 0.704 |
| Kurtosis: | 2.177 | Cond. No. | 1.89e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.83 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.52e-05 |
| Time: | 08:55:23 | Log-Likelihood: | -100.93 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -88.7975 | 294.269 | -0.302 | 0.766 | -702.632 525.038 |
| C(dose)[T.1] | 53.6259 | 8.739 | 6.137 | 0.000 | 35.397 71.855 |
| expression | 13.0211 | 26.788 | 0.486 | 0.632 | -42.859 68.901 |
| Omnibus: | 0.720 | Durbin-Watson: | 2.049 |
| Prob(Omnibus): | 0.698 | Jarque-Bera (JB): | 0.688 |
| Skew: | 0.101 | Prob(JB): | 0.709 |
| Kurtosis: | 2.177 | Cond. No. | 748. |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 08:55:23 | 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: | 0.001734 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.967 |
| Time: | 08:55:23 | 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 | 59.4819 | 485.954 | 0.122 | 0.904 | -951.114 1070.078 |
| expression | 1.8443 | 44.285 | 0.042 | 0.967 | -90.252 93.941 |
| Omnibus: | 3.309 | Durbin-Watson: | 2.495 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.572 |
| Skew: | 0.290 | Prob(JB): | 0.456 |
| Kurtosis: | 1.858 | Cond. No. | 745. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.594 | 0.053 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.620 |
| Model: | OLS | Adj. R-squared: | 0.517 |
| Method: | Least Squares | F-statistic: | 5.990 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0113 |
| Time: | 08:55:23 | Log-Likelihood: | -68.037 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1615.6824 | 1185.583 | -1.363 | 0.200 | -4225.133 993.768 |
| C(dose)[T.1] | 979.1526 | 1264.030 | 0.775 | 0.455 | -1802.958 3761.263 |
| expression | 144.2906 | 101.635 | 1.420 | 0.183 | -79.406 367.987 |
| expression:C(dose)[T.1] | -80.1286 | 108.276 | -0.740 | 0.475 | -318.442 158.184 |
| Omnibus: | 0.925 | Durbin-Watson: | 1.225 |
| Prob(Omnibus): | 0.630 | Jarque-Bera (JB): | 0.771 |
| Skew: | -0.272 | Prob(JB): | 0.680 |
| Kurtosis: | 2.031 | Cond. No. | 3.44e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.601 |
| Model: | OLS | Adj. R-squared: | 0.535 |
| Method: | Least Squares | F-statistic: | 9.052 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00401 |
| Time: | 08:55:23 | Log-Likelihood: | -68.402 |
| No. Observations: | 15 | AIC: | 142.8 |
| Df Residuals: | 12 | BIC: | 144.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -792.1419 | 401.146 | -1.975 | 0.072 | -1666.164 81.880 |
| C(dose)[T.1] | 43.7727 | 13.622 | 3.213 | 0.007 | 14.094 73.452 |
| expression | 73.6897 | 34.379 | 2.143 | 0.053 | -1.217 148.596 |
| Omnibus: | 1.125 | Durbin-Watson: | 1.033 |
| Prob(Omnibus): | 0.570 | Jarque-Bera (JB): | 0.888 |
| Skew: | -0.341 | Prob(JB): | 0.642 |
| Kurtosis: | 2.023 | Cond. No. | 709. |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 08:55:23 | 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: | 0.258 |
| Model: | OLS | Adj. R-squared: | 0.201 |
| Method: | Least Squares | F-statistic: | 4.529 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0530 |
| Time: | 08:55:23 | Log-Likelihood: | -73.058 |
| No. Observations: | 15 | AIC: | 150.1 |
| Df Residuals: | 13 | BIC: | 151.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1008.9904 | 518.209 | -1.947 | 0.073 | -2128.513 110.532 |
| expression | 94.2121 | 44.270 | 2.128 | 0.053 | -1.427 189.852 |
| Omnibus: | 0.816 | Durbin-Watson: | 1.661 |
| Prob(Omnibus): | 0.665 | Jarque-Bera (JB): | 0.777 |
| Skew: | 0.388 | Prob(JB): | 0.678 |
| Kurtosis: | 2.199 | Cond. No. | 698. |