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 |
| 7.449 | 0.013 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.757 |
| Model: | OLS | Adj. R-squared: | 0.718 |
| Method: | Least Squares | F-statistic: | 19.70 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.70e-06 |
| Time: | 23:00:42 | Log-Likelihood: | -96.851 |
| No. Observations: | 23 | AIC: | 201.7 |
| Df Residuals: | 19 | BIC: | 206.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.1431 | 25.984 | 1.006 | 0.327 | -28.242 80.528 |
| C(dose)[T.1] | 10.7320 | 37.405 | 0.287 | 0.777 | -67.558 89.022 |
| expression | 8.9081 | 8.082 | 1.102 | 0.284 | -8.007 25.824 |
| expression:C(dose)[T.1] | 10.7293 | 10.907 | 0.984 | 0.338 | -12.099 33.557 |
| Omnibus: | 1.159 | Durbin-Watson: | 1.546 |
| Prob(Omnibus): | 0.560 | Jarque-Bera (JB): | 1.069 |
| Skew: | 0.383 | Prob(JB): | 0.586 |
| Kurtosis: | 2.274 | Cond. No. | 49.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.744 |
| Model: | OLS | Adj. R-squared: | 0.719 |
| Method: | Least Squares | F-statistic: | 29.11 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.20e-06 |
| Time: | 23:00:42 | Log-Likelihood: | -97.422 |
| No. Observations: | 23 | AIC: | 200.8 |
| Df Residuals: | 20 | BIC: | 204.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.5824 | 17.851 | 0.425 | 0.676 | -29.655 44.819 |
| C(dose)[T.1] | 46.7035 | 7.871 | 5.934 | 0.000 | 30.286 63.121 |
| expression | 14.7994 | 5.423 | 2.729 | 0.013 | 3.488 26.111 |
| Omnibus: | 1.124 | Durbin-Watson: | 1.434 |
| Prob(Omnibus): | 0.570 | Jarque-Bera (JB): | 0.912 |
| Skew: | 0.223 | Prob(JB): | 0.634 |
| Kurtosis: | 2.132 | Cond. No. | 18.0 |
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: | 23:00:43 | 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: | 0.294 |
| Model: | OLS | Adj. R-squared: | 0.260 |
| Method: | Least Squares | F-statistic: | 8.749 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00751 |
| Time: | 23:00:43 | Log-Likelihood: | -109.10 |
| No. Observations: | 23 | AIC: | 222.2 |
| Df Residuals: | 21 | BIC: | 224.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.5179 | 28.785 | -0.122 | 0.904 | -63.381 56.345 |
| expression | 24.7362 | 8.363 | 2.958 | 0.008 | 7.345 42.127 |
| Omnibus: | 0.195 | Durbin-Watson: | 2.708 |
| Prob(Omnibus): | 0.907 | Jarque-Bera (JB): | 0.397 |
| Skew: | -0.108 | Prob(JB): | 0.820 |
| Kurtosis: | 2.394 | Cond. No. | 17.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.556 | 0.084 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.586 |
| Model: | OLS | Adj. R-squared: | 0.473 |
| Method: | Least Squares | F-statistic: | 5.187 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0178 |
| Time: | 23:00:43 | Log-Likelihood: | -68.688 |
| No. Observations: | 15 | AIC: | 145.4 |
| Df Residuals: | 11 | BIC: | 148.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -69.0424 | 90.497 | -0.763 | 0.462 | -268.225 130.141 |
| C(dose)[T.1] | 110.5568 | 111.820 | 0.989 | 0.344 | -135.557 356.670 |
| expression | 24.8738 | 16.385 | 1.518 | 0.157 | -11.189 60.937 |
| expression:C(dose)[T.1] | -11.0180 | 20.299 | -0.543 | 0.598 | -55.696 33.660 |
| Omnibus: | 0.051 | Durbin-Watson: | 1.468 |
| Prob(Omnibus): | 0.975 | Jarque-Bera (JB): | 0.267 |
| Skew: | -0.073 | Prob(JB): | 0.875 |
| Kurtosis: | 2.364 | Cond. No. | 127. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.575 |
| Model: | OLS | Adj. R-squared: | 0.504 |
| Method: | Least Squares | F-statistic: | 8.110 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00591 |
| Time: | 23:00:43 | Log-Likelihood: | -68.886 |
| No. Observations: | 15 | AIC: | 143.8 |
| Df Residuals: | 12 | BIC: | 145.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -29.6558 | 52.463 | -0.565 | 0.582 | -143.963 84.652 |
| C(dose)[T.1] | 50.3583 | 13.838 | 3.639 | 0.003 | 20.208 80.508 |
| expression | 17.6950 | 9.383 | 1.886 | 0.084 | -2.750 38.140 |
| Omnibus: | 0.006 | Durbin-Watson: | 1.433 |
| Prob(Omnibus): | 0.997 | Jarque-Bera (JB): | 0.185 |
| Skew: | 0.037 | Prob(JB): | 0.912 |
| Kurtosis: | 2.461 | Cond. No. | 43.4 |
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: | 23:00:43 | 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: | 0.105 |
| Model: | OLS | Adj. R-squared: | 0.037 |
| Method: | Least Squares | F-statistic: | 1.533 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.238 |
| Time: | 23:00:43 | Log-Likelihood: | -74.464 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 5.4906 | 71.858 | 0.076 | 0.940 | -149.749 160.730 |
| expression | 16.1746 | 13.063 | 1.238 | 0.238 | -12.046 44.395 |
| Omnibus: | 1.558 | Durbin-Watson: | 1.845 |
| Prob(Omnibus): | 0.459 | Jarque-Bera (JB): | 1.234 |
| Skew: | 0.547 | Prob(JB): | 0.540 |
| Kurtosis: | 2.118 | Cond. No. | 42.5 |