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 |
| 0.417 | 0.526 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.09 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000118 |
| Time: | 22:47:55 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -33.8849 | 198.584 | -0.171 | 0.866 | -449.526 381.756 |
| C(dose)[T.1] | 30.7854 | 316.625 | 0.097 | 0.924 | -631.919 693.489 |
| expression | 10.6137 | 23.914 | 0.444 | 0.662 | -39.440 60.667 |
| expression:C(dose)[T.1] | 1.7712 | 36.515 | 0.049 | 0.962 | -74.655 78.198 |
| Omnibus: | 0.066 | Durbin-Watson: | 1.918 |
| Prob(Omnibus): | 0.968 | Jarque-Bera (JB): | 0.281 |
| Skew: | 0.057 | Prob(JB): | 0.869 |
| Kurtosis: | 2.471 | Cond. No. | 782. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.09 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.31e-05 |
| Time: | 22:47:55 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.1902 | 146.332 | -0.275 | 0.786 | -345.433 265.052 |
| C(dose)[T.1] | 46.1272 | 14.144 | 3.261 | 0.004 | 16.624 75.630 |
| expression | 11.3733 | 17.616 | 0.646 | 0.526 | -25.372 48.119 |
| Omnibus: | 0.065 | Durbin-Watson: | 1.919 |
| Prob(Omnibus): | 0.968 | Jarque-Bera (JB): | 0.284 |
| Skew: | 0.044 | Prob(JB): | 0.867 |
| Kurtosis: | 2.462 | 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: | 22:47:55 | 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: | 0.473 |
| Model: | OLS | Adj. R-squared: | 0.448 |
| Method: | Least Squares | F-statistic: | 18.88 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000285 |
| Time: | 22:47:55 | Log-Likelihood: | -105.73 |
| No. Observations: | 23 | AIC: | 215.5 |
| Df Residuals: | 21 | BIC: | 217.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -408.3635 | 112.456 | -3.631 | 0.002 | -642.229 -174.498 |
| expression | 56.7327 | 13.057 | 4.345 | 0.000 | 29.579 83.887 |
| Omnibus: | 1.068 | Durbin-Watson: | 2.134 |
| Prob(Omnibus): | 0.586 | Jarque-Bera (JB): | 0.306 |
| Skew: | 0.260 | Prob(JB): | 0.858 |
| Kurtosis: | 3.224 | Cond. No. | 187. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.866 | 0.116 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.741 |
| Model: | OLS | Adj. R-squared: | 0.671 |
| Method: | Least Squares | F-statistic: | 10.51 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00147 |
| Time: | 22:47:56 | Log-Likelihood: | -65.157 |
| No. Observations: | 15 | AIC: | 138.3 |
| Df Residuals: | 11 | BIC: | 141.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -24.9236 | 134.339 | -0.186 | 0.856 | -320.602 270.755 |
| C(dose)[T.1] | 577.9435 | 184.424 | 3.134 | 0.010 | 172.029 983.858 |
| expression | 13.5767 | 19.712 | 0.689 | 0.505 | -29.810 56.963 |
| expression:C(dose)[T.1] | -74.3163 | 26.395 | -2.816 | 0.017 | -132.412 -16.221 |
| Omnibus: | 1.039 | Durbin-Watson: | 1.437 |
| Prob(Omnibus): | 0.595 | Jarque-Bera (JB): | 0.413 |
| Skew: | -0.405 | Prob(JB): | 0.814 |
| Kurtosis: | 2.932 | Cond. No. | 321. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.555 |
| Model: | OLS | Adj. R-squared: | 0.481 |
| Method: | Least Squares | F-statistic: | 7.484 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00776 |
| Time: | 22:47:56 | Log-Likelihood: | -69.227 |
| No. Observations: | 15 | AIC: | 144.5 |
| Df Residuals: | 12 | BIC: | 146.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 257.0152 | 112.467 | 2.285 | 0.041 | 11.971 502.059 |
| C(dose)[T.1] | 59.8559 | 15.480 | 3.867 | 0.002 | 26.128 93.584 |
| expression | -27.8712 | 16.464 | -1.693 | 0.116 | -63.743 8.001 |
| Omnibus: | 3.505 | Durbin-Watson: | 1.156 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 2.458 |
| Skew: | -0.974 | Prob(JB): | 0.293 |
| Kurtosis: | 2.624 | Cond. No. | 115. |
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: | 22:47:56 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.008325 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.929 |
| Time: | 22:47:56 | Log-Likelihood: | -75.295 |
| 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 | 107.5105 | 152.065 | 0.707 | 0.492 | -221.006 436.027 |
| expression | -1.9759 | 21.656 | -0.091 | 0.929 | -48.761 44.809 |
| Omnibus: | 0.519 | Durbin-Watson: | 1.652 |
| Prob(Omnibus): | 0.772 | Jarque-Bera (JB): | 0.547 |
| Skew: | 0.014 | Prob(JB): | 0.761 |
| Kurtosis: | 2.065 | Cond. No. | 107. |