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 |
| 0.023 | 0.881 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 13.27 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 6.64e-05 |
| Time: | 22:56:44 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 208.2 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -100.7794 | 183.031 | -0.551 | 0.588 | -483.867 282.308 |
| C(dose)[T.1] | 462.1493 | 322.294 | 1.434 | 0.168 | -212.419 1136.718 |
| expression | 18.6172 | 21.974 | 0.847 | 0.407 | -27.375 64.609 |
| expression:C(dose)[T.1] | -49.0787 | 38.676 | -1.269 | 0.220 | -130.029 31.871 |
| Omnibus: | 0.079 | Durbin-Watson: | 1.660 |
| Prob(Omnibus): | 0.961 | Jarque-Bera (JB): | 0.203 |
| Skew: | 0.119 | Prob(JB): | 0.904 |
| Kurtosis: | 2.607 | Cond. No. | 761. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.80e-05 |
| Time: | 22:56:44 | 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 | 31.1102 | 152.939 | 0.203 | 0.841 | -287.916 350.136 |
| C(dose)[T.1] | 53.3159 | 8.766 | 6.082 | 0.000 | 35.030 71.601 |
| expression | 2.7746 | 18.357 | 0.151 | 0.881 | -35.517 41.066 |
| Omnibus: | 0.216 | Durbin-Watson: | 1.909 |
| Prob(Omnibus): | 0.897 | Jarque-Bera (JB): | 0.417 |
| Skew: | 0.070 | Prob(JB): | 0.812 |
| Kurtosis: | 2.355 | Cond. No. | 296. |
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: | 22:56:44 | 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: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02280 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.881 |
| Time: | 22:56:44 | Log-Likelihood: | -113.09 |
| 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 | 41.6921 | 251.936 | 0.165 | 0.870 | -482.237 565.621 |
| expression | 4.5656 | 30.237 | 0.151 | 0.881 | -58.316 67.447 |
| Omnibus: | 3.196 | Durbin-Watson: | 2.486 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.595 |
| Skew: | 0.318 | Prob(JB): | 0.450 |
| Kurtosis: | 1.877 | Cond. No. | 295. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.811 | 0.385 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.529 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 4.125 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0346 |
| Time: | 22:56:44 | Log-Likelihood: | -69.647 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.0543 | 241.351 | 0.352 | 0.731 | -446.156 616.265 |
| C(dose)[T.1] | -320.9101 | 352.822 | -0.910 | 0.383 | -1097.465 455.645 |
| expression | -2.2286 | 30.484 | -0.073 | 0.943 | -69.324 64.867 |
| expression:C(dose)[T.1] | 45.1869 | 43.720 | 1.034 | 0.324 | -51.041 141.415 |
| Omnibus: | 1.654 | Durbin-Watson: | 0.931 |
| Prob(Omnibus): | 0.437 | Jarque-Bera (JB): | 1.158 |
| Skew: | -0.443 | Prob(JB): | 0.560 |
| Kurtosis: | 1.967 | Cond. No. | 504. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.621 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0189 |
| Time: | 22:56:44 | Log-Likelihood: | -70.342 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -88.6907 | 173.670 | -0.511 | 0.619 | -467.084 289.703 |
| C(dose)[T.1] | 43.3463 | 16.560 | 2.618 | 0.022 | 7.266 79.427 |
| expression | 19.7399 | 21.914 | 0.901 | 0.385 | -28.006 67.486 |
| Omnibus: | 2.300 | Durbin-Watson: | 0.714 |
| Prob(Omnibus): | 0.317 | Jarque-Bera (JB): | 1.770 |
| Skew: | -0.745 | Prob(JB): | 0.413 |
| Kurtosis: | 2.218 | Cond. No. | 188. |
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: | 22:56:44 | 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: | 0.189 |
| Model: | OLS | Adj. R-squared: | 0.126 |
| Method: | Least Squares | F-statistic: | 3.027 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.105 |
| Time: | 22:56:44 | Log-Likelihood: | -73.730 |
| No. Observations: | 15 | AIC: | 151.5 |
| Df Residuals: | 13 | BIC: | 152.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -247.0460 | 196.037 | -1.260 | 0.230 | -670.557 176.465 |
| expression | 42.2360 | 24.275 | 1.740 | 0.105 | -10.207 94.679 |
| Omnibus: | 0.202 | Durbin-Watson: | 1.294 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.230 |
| Skew: | -0.211 | Prob(JB): | 0.891 |
| Kurtosis: | 2.563 | Cond. No. | 176. |