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 |
| 1.203 | 0.286 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 12.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.86e-05 |
| Time: | 04:44:11 | Log-Likelihood: | -100.32 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -126.4560 | 167.572 | -0.755 | 0.460 | -477.188 224.276 |
| C(dose)[T.1] | 149.4935 | 318.804 | 0.469 | 0.644 | -517.770 816.757 |
| expression | 20.9280 | 19.399 | 1.079 | 0.294 | -19.674 61.530 |
| expression:C(dose)[T.1] | -11.7540 | 35.252 | -0.333 | 0.742 | -85.537 62.029 |
| Omnibus: | 0.659 | Durbin-Watson: | 1.498 |
| Prob(Omnibus): | 0.719 | Jarque-Bera (JB): | 0.677 |
| Skew: | -0.147 | Prob(JB): | 0.713 |
| Kurtosis: | 2.213 | Cond. No. | 795. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.58e-05 |
| Time: | 04:44:11 | Log-Likelihood: | -100.39 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -95.7289 | 136.812 | -0.700 | 0.492 | -381.113 189.655 |
| C(dose)[T.1] | 43.2803 | 12.514 | 3.459 | 0.002 | 17.177 69.384 |
| expression | 17.3686 | 15.833 | 1.097 | 0.286 | -15.659 50.397 |
| Omnibus: | 0.648 | Durbin-Watson: | 1.642 |
| Prob(Omnibus): | 0.723 | Jarque-Bera (JB): | 0.661 |
| Skew: | -0.116 | Prob(JB): | 0.719 |
| Kurtosis: | 2.203 | Cond. No. | 292. |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:44:11 | 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: | 0.471 |
| Model: | OLS | Adj. R-squared: | 0.446 |
| Method: | Least Squares | F-statistic: | 18.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000299 |
| Time: | 04:44:11 | Log-Likelihood: | -105.78 |
| No. Observations: | 23 | AIC: | 215.6 |
| Df Residuals: | 21 | BIC: | 217.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -432.4779 | 118.572 | -3.647 | 0.002 | -679.061 -185.895 |
| expression | 57.4881 | 13.295 | 4.324 | 0.000 | 29.839 85.137 |
| Omnibus: | 0.965 | Durbin-Watson: | 1.555 |
| Prob(Omnibus): | 0.617 | Jarque-Bera (JB): | 0.769 |
| Skew: | -0.054 | Prob(JB): | 0.681 |
| Kurtosis: | 2.111 | Cond. No. | 204. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.136 | 0.719 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.478 |
| Model: | OLS | Adj. R-squared: | 0.336 |
| Method: | Least Squares | F-statistic: | 3.357 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0590 |
| Time: | 04:44:11 | Log-Likelihood: | -70.425 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.2877 | 149.349 | 0.430 | 0.675 | -264.428 393.003 |
| C(dose)[T.1] | 253.6873 | 297.218 | 0.854 | 0.412 | -400.486 907.861 |
| expression | 0.3902 | 18.499 | 0.021 | 0.984 | -40.327 41.107 |
| expression:C(dose)[T.1] | -26.6658 | 38.271 | -0.697 | 0.500 | -110.901 57.569 |
| Omnibus: | 2.816 | Durbin-Watson: | 0.684 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.923 |
| Skew: | -0.858 | Prob(JB): | 0.382 |
| Kurtosis: | 2.641 | Cond. No. | 355. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.008 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0262 |
| Time: | 04:44:11 | Log-Likelihood: | -70.749 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.4332 | 128.028 | 0.894 | 0.389 | -164.516 393.382 |
| C(dose)[T.1] | 46.9455 | 16.800 | 2.794 | 0.016 | 10.341 83.550 |
| expression | -5.8402 | 15.844 | -0.369 | 0.719 | -40.361 28.680 |
| Omnibus: | 2.309 | Durbin-Watson: | 0.733 |
| Prob(Omnibus): | 0.315 | Jarque-Bera (JB): | 1.692 |
| Skew: | -0.780 | Prob(JB): | 0.429 |
| Kurtosis: | 2.475 | Cond. No. | 131. |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:44:11 | 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: | 0.100 |
| Model: | OLS | Adj. R-squared: | 0.031 |
| Method: | Least Squares | F-statistic: | 1.449 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.250 |
| Time: | 04:44:11 | Log-Likelihood: | -74.507 |
| No. Observations: | 15 | AIC: | 153.0 |
| Df Residuals: | 13 | BIC: | 154.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 265.6753 | 143.219 | 1.855 | 0.086 | -43.731 575.082 |
| expression | -21.9318 | 18.220 | -1.204 | 0.250 | -61.293 17.429 |
| Omnibus: | 2.172 | Durbin-Watson: | 1.188 |
| Prob(Omnibus): | 0.338 | Jarque-Bera (JB): | 0.996 |
| Skew: | -0.102 | Prob(JB): | 0.608 |
| Kurtosis: | 1.755 | Cond. No. | 119. |