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 |
| 0.029 | 0.867 | 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: | 22:48:44 | 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 | 116.2518 | 136.316 | 0.853 | 0.404 | -169.060 401.564 |
| C(dose)[T.1] | -19.1637 | 165.471 | -0.116 | 0.909 | -365.498 327.170 |
| expression | -8.5832 | 18.839 | -0.456 | 0.654 | -48.013 30.847 |
| expression:C(dose)[T.1] | 10.0303 | 22.860 | 0.439 | 0.666 | -37.817 57.878 |
| Omnibus: | 0.062 | Durbin-Watson: | 1.825 |
| Prob(Omnibus): | 0.969 | Jarque-Bera (JB): | 0.284 |
| Skew: | 0.030 | Prob(JB): | 0.868 |
| Kurtosis: | 2.459 | Cond. No. | 384. |
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: | 18.54 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.79e-05 |
| Time: | 22:48:44 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.0146 | 75.811 | 0.884 | 0.387 | -91.124 225.153 |
| C(dose)[T.1] | 53.3327 | 8.764 | 6.086 | 0.000 | 35.052 71.613 |
| expression | -1.7716 | 10.454 | -0.169 | 0.867 | -23.579 20.035 |
| Omnibus: | 0.407 | Durbin-Watson: | 1.894 |
| Prob(Omnibus): | 0.816 | Jarque-Bera (JB): | 0.535 |
| Skew: | 0.083 | Prob(JB): | 0.765 |
| Kurtosis: | 2.271 | Cond. No. | 128. |
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: | 22:48:44 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01295 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.910 |
| Time: | 22:48:44 | 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 | 93.8856 | 124.726 | 0.753 | 0.460 | -165.497 353.268 |
| expression | -1.9604 | 17.229 | -0.114 | 0.910 | -37.789 33.869 |
| Omnibus: | 3.168 | Durbin-Watson: | 2.488 |
| Prob(Omnibus): | 0.205 | Jarque-Bera (JB): | 1.561 |
| Skew: | 0.301 | Prob(JB): | 0.458 |
| Kurtosis: | 1.875 | Cond. No. | 128. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.972 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.565 |
| Model: | OLS | Adj. R-squared: | 0.447 |
| Method: | Least Squares | F-statistic: | 4.766 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0230 |
| Time: | 22:48:44 | Log-Likelihood: | -69.054 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 11 | BIC: | 148.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 601.7159 | 347.953 | 1.729 | 0.112 | -164.124 1367.556 |
| C(dose)[T.1] | -636.4721 | 400.429 | -1.589 | 0.140 | -1517.810 244.866 |
| expression | -74.6298 | 48.580 | -1.536 | 0.153 | -181.553 32.293 |
| expression:C(dose)[T.1] | 94.1673 | 54.886 | 1.716 | 0.114 | -26.636 214.971 |
| Omnibus: | 1.761 | Durbin-Watson: | 1.068 |
| Prob(Omnibus): | 0.415 | Jarque-Bera (JB): | 0.830 |
| Skew: | -0.576 | Prob(JB): | 0.660 |
| Kurtosis: | 2.982 | Cond. No. | 635. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.886 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 22:48:44 | Log-Likelihood: | -70.832 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.5756 | 174.855 | 0.421 | 0.681 | -307.400 454.552 |
| C(dose)[T.1] | 49.7022 | 21.303 | 2.333 | 0.038 | 3.287 96.118 |
| expression | -0.8586 | 24.371 | -0.035 | 0.972 | -53.959 52.241 |
| Omnibus: | 2.759 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.897 |
| Skew: | -0.851 | Prob(JB): | 0.387 |
| Kurtosis: | 2.625 | Cond. No. | 171. |
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: | 22:48:44 | 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: | 0.199 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 3.226 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0957 |
| Time: | 22:48:44 | Log-Likelihood: | -73.638 |
| No. Observations: | 15 | AIC: | 151.3 |
| Df Residuals: | 13 | BIC: | 152.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -186.2888 | 156.135 | -1.193 | 0.254 | -523.599 151.021 |
| expression | 37.4605 | 20.857 | 1.796 | 0.096 | -7.598 82.519 |
| Omnibus: | 0.240 | Durbin-Watson: | 0.977 |
| Prob(Omnibus): | 0.887 | Jarque-Bera (JB): | 0.180 |
| Skew: | -0.204 | Prob(JB): | 0.914 |
| Kurtosis: | 2.651 | Cond. No. | 131. |