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 |
| 3.750 | 0.067 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.707 |
| Model: | OLS | Adj. R-squared: | 0.661 |
| Method: | Least Squares | F-statistic: | 15.27 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.68e-05 |
| Time: | 22:52:12 | Log-Likelihood: | -98.993 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 19 | BIC: | 210.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -56.2264 | 64.574 | -0.871 | 0.395 | -191.382 78.929 |
| C(dose)[T.1] | 93.5268 | 101.868 | 0.918 | 0.370 | -119.686 306.740 |
| expression | 20.3810 | 11.871 | 1.717 | 0.102 | -4.465 45.227 |
| expression:C(dose)[T.1] | -7.3965 | 18.757 | -0.394 | 0.698 | -46.655 31.862 |
| Omnibus: | 0.345 | Durbin-Watson: | 2.215 |
| Prob(Omnibus): | 0.842 | Jarque-Bera (JB): | 0.423 |
| Skew: | -0.248 | Prob(JB): | 0.809 |
| Kurtosis: | 2.559 | Cond. No. | 173. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.704 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 23.84 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.08e-06 |
| Time: | 22:52:12 | Log-Likelihood: | -99.086 |
| No. Observations: | 23 | AIC: | 204.2 |
| Df Residuals: | 20 | BIC: | 207.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.1726 | 49.055 | -0.819 | 0.422 | -142.500 62.155 |
| C(dose)[T.1] | 53.4870 | 8.048 | 6.646 | 0.000 | 36.699 70.275 |
| expression | 17.4183 | 8.995 | 1.936 | 0.067 | -1.345 36.181 |
| Omnibus: | 0.206 | Durbin-Watson: | 2.214 |
| Prob(Omnibus): | 0.902 | Jarque-Bera (JB): | 0.277 |
| Skew: | -0.192 | Prob(JB): | 0.871 |
| Kurtosis: | 2.624 | Cond. No. | 68.8 |
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: | 22:52:12 | 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: | 0.052 |
| Model: | OLS | Adj. R-squared: | 0.007 |
| Method: | Least Squares | F-statistic: | 1.148 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.296 |
| Time: | 22:52:12 | Log-Likelihood: | -112.49 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.4799 | 85.417 | -0.134 | 0.894 | -189.115 166.155 |
| expression | 16.8435 | 15.722 | 1.071 | 0.296 | -15.853 49.540 |
| Omnibus: | 3.536 | Durbin-Watson: | 2.531 |
| Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 1.721 |
| Skew: | 0.349 | Prob(JB): | 0.423 |
| Kurtosis: | 1.856 | Cond. No. | 68.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.578 | 0.233 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.513 |
| Model: | OLS | Adj. R-squared: | 0.380 |
| Method: | Least Squares | F-statistic: | 3.865 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0412 |
| Time: | 22:52:12 | Log-Likelihood: | -69.902 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.9621 | 114.283 | -0.411 | 0.689 | -298.496 204.572 |
| C(dose)[T.1] | 69.5047 | 182.131 | 0.382 | 0.710 | -331.364 470.373 |
| expression | 23.1607 | 23.026 | 1.006 | 0.336 | -27.519 73.840 |
| expression:C(dose)[T.1] | -3.1230 | 37.913 | -0.082 | 0.936 | -86.569 80.323 |
| Omnibus: | 2.456 | Durbin-Watson: | 0.722 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.720 |
| Skew: | -0.802 | Prob(JB): | 0.423 |
| Kurtosis: | 2.576 | Cond. No. | 149. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.513 |
| Model: | OLS | Adj. R-squared: | 0.432 |
| Method: | Least Squares | F-statistic: | 6.316 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0134 |
| Time: | 22:52:12 | Log-Likelihood: | -69.906 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.2727 | 87.200 | -0.473 | 0.644 | -231.266 148.721 |
| C(dose)[T.1] | 54.5606 | 15.400 | 3.543 | 0.004 | 21.006 88.115 |
| expression | 22.0088 | 17.519 | 1.256 | 0.233 | -16.163 60.180 |
| Omnibus: | 2.504 | Durbin-Watson: | 0.708 |
| Prob(Omnibus): | 0.286 | Jarque-Bera (JB): | 1.748 |
| Skew: | -0.810 | Prob(JB): | 0.417 |
| Kurtosis: | 2.584 | Cond. No. | 59.8 |
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: | 22:52:12 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04305 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.839 |
| Time: | 22:52:12 | Log-Likelihood: | -75.275 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.5845 | 111.704 | 0.632 | 0.538 | -170.738 311.907 |
| expression | 4.7998 | 23.132 | 0.207 | 0.839 | -45.174 54.774 |
| Omnibus: | 0.859 | Durbin-Watson: | 1.640 |
| Prob(Omnibus): | 0.651 | Jarque-Bera (JB): | 0.675 |
| Skew: | 0.092 | Prob(JB): | 0.713 |
| Kurtosis: | 1.977 | Cond. No. | 55.4 |