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 |
| 0.073 | 0.790 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.92 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000128 |
| Time: | 22:53:03 | Log-Likelihood: | -100.93 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.2192 | 73.434 | 0.303 | 0.766 | -131.481 175.919 |
| C(dose)[T.1] | 105.4235 | 135.439 | 0.778 | 0.446 | -178.054 388.900 |
| expression | 4.9212 | 11.257 | 0.437 | 0.667 | -18.640 28.483 |
| expression:C(dose)[T.1] | -7.8742 | 20.135 | -0.391 | 0.700 | -50.018 34.269 |
| Omnibus: | 1.001 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.606 | Jarque-Bera (JB): | 0.789 |
| Skew: | 0.081 | Prob(JB): | 0.674 |
| Kurtosis: | 2.107 | Cond. No. | 249. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.73e-05 |
| Time: | 22:53:04 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.2174 | 59.679 | 0.640 | 0.529 | -86.270 162.705 |
| C(dose)[T.1] | 52.5854 | 9.188 | 5.723 | 0.000 | 33.419 71.751 |
| expression | 2.4601 | 9.134 | 0.269 | 0.790 | -16.592 21.513 |
| Omnibus: | 0.374 | Durbin-Watson: | 1.908 |
| Prob(Omnibus): | 0.829 | Jarque-Bera (JB): | 0.513 |
| Skew: | 0.032 | Prob(JB): | 0.774 |
| Kurtosis: | 2.271 | Cond. No. | 93.3 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:53:04 | 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: | 0.078 |
| Model: | OLS | Adj. R-squared: | 0.034 |
| Method: | Least Squares | F-statistic: | 1.768 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.198 |
| Time: | 22:53:04 | Log-Likelihood: | -112.18 |
| No. Observations: | 23 | AIC: | 228.4 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -42.1646 | 91.932 | -0.459 | 0.651 | -233.348 149.019 |
| expression | 18.3382 | 13.793 | 1.330 | 0.198 | -10.345 47.022 |
| Omnibus: | 4.813 | Durbin-Watson: | 2.753 |
| Prob(Omnibus): | 0.090 | Jarque-Bera (JB): | 1.847 |
| Skew: | 0.297 | Prob(JB): | 0.397 |
| Kurtosis: | 1.746 | Cond. No. | 90.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.557 | 0.136 | 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: | 4.403 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0289 |
| Time: | 22:53:04 | Log-Likelihood: | -69.384 |
| 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 | -67.9390 | 126.676 | -0.536 | 0.602 | -346.750 210.873 |
| C(dose)[T.1] | 53.4187 | 174.806 | 0.306 | 0.766 | -331.326 438.164 |
| expression | 21.7896 | 20.315 | 1.073 | 0.306 | -22.923 66.502 |
| expression:C(dose)[T.1] | -0.4455 | 28.183 | -0.016 | 0.988 | -62.476 61.585 |
| Omnibus: | 1.954 | Durbin-Watson: | 1.183 |
| Prob(Omnibus): | 0.376 | Jarque-Bera (JB): | 1.196 |
| Skew: | -0.405 | Prob(JB): | 0.550 |
| Kurtosis: | 1.879 | Cond. No. | 200. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.546 |
| Model: | OLS | Adj. R-squared: | 0.470 |
| Method: | Least Squares | F-statistic: | 7.204 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00880 |
| Time: | 22:53:04 | Log-Likelihood: | -69.384 |
| No. Observations: | 15 | AIC: | 144.8 |
| Df Residuals: | 12 | BIC: | 146.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -66.5010 | 84.401 | -0.788 | 0.446 | -250.396 117.394 |
| C(dose)[T.1] | 50.6658 | 14.320 | 3.538 | 0.004 | 19.465 81.866 |
| expression | 21.5582 | 13.481 | 1.599 | 0.136 | -7.816 50.932 |
| Omnibus: | 1.943 | Durbin-Watson: | 1.179 |
| Prob(Omnibus): | 0.378 | Jarque-Bera (JB): | 1.191 |
| Skew: | -0.403 | Prob(JB): | 0.551 |
| Kurtosis: | 1.880 | Cond. No. | 75.5 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:53:04 | 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: | 0.072 |
| Model: | OLS | Adj. R-squared: | 0.000 |
| Method: | Least Squares | F-statistic: | 1.002 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.335 |
| Time: | 22:53:04 | Log-Likelihood: | -74.743 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -20.5764 | 114.531 | -0.180 | 0.860 | -268.006 226.853 |
| expression | 18.4975 | 18.476 | 1.001 | 0.335 | -21.418 58.413 |
| Omnibus: | 1.980 | Durbin-Watson: | 1.914 |
| Prob(Omnibus): | 0.372 | Jarque-Bera (JB): | 1.077 |
| Skew: | 0.290 | Prob(JB): | 0.584 |
| Kurtosis: | 1.822 | Cond. No. | 74.4 |