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 |
| 0.463 | 0.504 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.825 |
| Model: | OLS | Adj. R-squared: | 0.797 |
| Method: | Least Squares | F-statistic: | 29.86 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.13e-07 |
| Time: | 22:45:55 | Log-Likelihood: | -93.060 |
| No. Observations: | 23 | AIC: | 194.1 |
| Df Residuals: | 19 | BIC: | 198.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -369.9542 | 250.695 | -1.476 | 0.156 | -894.664 154.756 |
| C(dose)[T.1] | 1949.6535 | 443.071 | 4.400 | 0.000 | 1022.295 2877.012 |
| expression | 41.2171 | 24.357 | 1.692 | 0.107 | -9.763 92.197 |
| expression:C(dose)[T.1] | -181.1547 | 42.415 | -4.271 | 0.000 | -269.930 -92.379 |
| Omnibus: | 8.108 | Durbin-Watson: | 2.033 |
| Prob(Omnibus): | 0.017 | Jarque-Bera (JB): | 5.796 |
| Skew: | 1.060 | Prob(JB): | 0.0551 |
| Kurtosis: | 4.246 | Cond. No. | 1.77e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.15 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.25e-05 |
| Time: | 22:45:55 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 244.8210 | 280.085 | 0.874 | 0.392 | -339.425 829.067 |
| C(dose)[T.1] | 57.5812 | 10.679 | 5.392 | 0.000 | 35.305 79.857 |
| expression | -18.5224 | 27.210 | -0.681 | 0.504 | -75.282 38.237 |
| Omnibus: | 2.245 | Durbin-Watson: | 1.907 |
| Prob(Omnibus): | 0.325 | Jarque-Bera (JB): | 1.164 |
| Skew: | 0.129 | Prob(JB): | 0.559 |
| Kurtosis: | 1.929 | Cond. No. | 680. |
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: | 22:45:55 | 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: | 0.158 |
| Model: | OLS | Adj. R-squared: | 0.118 |
| Method: | Least Squares | F-statistic: | 3.952 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0600 |
| Time: | 22:45:55 | Log-Likelihood: | -111.12 |
| No. Observations: | 23 | AIC: | 226.2 |
| Df Residuals: | 21 | BIC: | 228.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -618.5427 | 351.293 | -1.761 | 0.093 | -1349.096 112.010 |
| expression | 67.1370 | 33.770 | 1.988 | 0.060 | -3.092 137.366 |
| Omnibus: | 2.076 | Durbin-Watson: | 1.885 |
| Prob(Omnibus): | 0.354 | Jarque-Bera (JB): | 1.788 |
| Skew: | 0.599 | Prob(JB): | 0.409 |
| Kurtosis: | 2.342 | Cond. No. | 557. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 7.492 | 0.018 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.721 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 9.457 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00222 |
| Time: | 22:45:55 | Log-Likelihood: | -65.737 |
| No. Observations: | 15 | AIC: | 139.5 |
| Df Residuals: | 11 | BIC: | 142.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 278.2268 | 223.699 | 1.244 | 0.239 | -214.132 770.586 |
| C(dose)[T.1] | 518.8327 | 311.910 | 1.663 | 0.124 | -167.678 1205.343 |
| expression | -22.3823 | 23.735 | -0.943 | 0.366 | -74.622 29.858 |
| expression:C(dose)[T.1] | -51.3567 | 33.428 | -1.536 | 0.153 | -124.932 22.219 |
| Omnibus: | 0.731 | Durbin-Watson: | 0.632 |
| Prob(Omnibus): | 0.694 | Jarque-Bera (JB): | 0.674 |
| Skew: | -0.422 | Prob(JB): | 0.714 |
| Kurtosis: | 2.395 | Cond. No. | 671. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 11.68 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00153 |
| Time: | 22:45:55 | Log-Likelihood: | -67.195 |
| No. Observations: | 15 | AIC: | 140.4 |
| Df Residuals: | 12 | BIC: | 142.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 522.0619 | 166.337 | 3.139 | 0.009 | 159.644 884.480 |
| C(dose)[T.1] | 40.0027 | 12.798 | 3.126 | 0.009 | 12.118 67.888 |
| expression | -48.2725 | 17.636 | -2.737 | 0.018 | -86.697 -9.848 |
| Omnibus: | 2.309 | Durbin-Watson: | 0.539 |
| Prob(Omnibus): | 0.315 | Jarque-Bera (JB): | 1.048 |
| Skew: | -0.160 | Prob(JB): | 0.592 |
| Kurtosis: | 1.745 | Cond. No. | 255. |
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: | 22:45:56 | 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: | 0.384 |
| Model: | OLS | Adj. R-squared: | 0.337 |
| Method: | Least Squares | F-statistic: | 8.117 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0137 |
| Time: | 22:45:56 | Log-Likelihood: | -71.662 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 13 | BIC: | 148.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 678.1733 | 205.318 | 3.303 | 0.006 | 234.611 1121.736 |
| expression | -62.7390 | 22.022 | -2.849 | 0.014 | -110.314 -15.164 |
| Omnibus: | 0.544 | Durbin-Watson: | 1.439 |
| Prob(Omnibus): | 0.762 | Jarque-Bera (JB): | 0.606 |
| Skew: | -0.279 | Prob(JB): | 0.739 |
| Kurtosis: | 2.189 | Cond. No. | 243. |