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 |
| 0.842 | 0.370 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 12.85 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 8.11e-05 |
| Time: | 21:27:06 | Log-Likelihood: | -100.36 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.6337 | 89.539 | 0.532 | 0.601 | -139.773 235.040 |
| C(dose)[T.1] | 123.5610 | 107.058 | 1.154 | 0.263 | -100.514 347.637 |
| expression | 1.5803 | 21.473 | 0.074 | 0.942 | -43.364 46.525 |
| expression:C(dose)[T.1] | -15.3486 | 24.908 | -0.616 | 0.545 | -67.482 36.785 |
| Omnibus: | 0.641 | Durbin-Watson: | 1.944 |
| Prob(Omnibus): | 0.726 | Jarque-Bera (JB): | 0.689 |
| Skew: | 0.199 | Prob(JB): | 0.709 |
| Kurtosis: | 2.252 | Cond. No. | 165. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.69 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 1.88e-05 |
| Time: | 21:27:06 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.0918 | 44.955 | 2.115 | 0.047 | 1.317 188.867 |
| C(dose)[T.1] | 57.8837 | 9.918 | 5.836 | 0.000 | 37.196 78.572 |
| expression | -9.8272 | 10.711 | -0.917 | 0.370 | -32.170 12.516 |
| Omnibus: | 0.572 | Durbin-Watson: | 1.862 |
| Prob(Omnibus): | 0.751 | Jarque-Bera (JB): | 0.638 |
| Skew: | 0.156 | Prob(JB): | 0.727 |
| Kurtosis: | 2.246 | Cond. No. | 49.1 |
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: | Wed, 09 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 21:27:06 | 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: | 0.090 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 2.068 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 0.165 |
| Time: | 21:27:06 | Log-Likelihood: | -112.02 |
| No. Observations: | 23 | AIC: | 228.0 |
| Df Residuals: | 21 | BIC: | 230.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.0868 | 65.590 | -0.215 | 0.832 | -150.488 122.314 |
| expression | 21.4090 | 14.887 | 1.438 | 0.165 | -9.550 52.368 |
| Omnibus: | 4.091 | Durbin-Watson: | 2.180 |
| Prob(Omnibus): | 0.129 | Jarque-Bera (JB): | 1.862 |
| Skew: | 0.366 | Prob(JB): | 0.394 |
| Kurtosis: | 1.814 | Cond. No. | 44.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.134 | 0.721 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.536 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 4.235 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 0.0322 |
| Time: | 21:27:06 | Log-Likelihood: | -69.542 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 125.1023 | 73.101 | 1.711 | 0.115 | -35.791 285.996 |
| C(dose)[T.1] | -93.8794 | 102.724 | -0.914 | 0.380 | -319.972 132.214 |
| expression | -12.0876 | 15.146 | -0.798 | 0.442 | -45.423 21.248 |
| expression:C(dose)[T.1] | 27.8119 | 20.059 | 1.386 | 0.193 | -16.338 71.962 |
| Omnibus: | 1.570 | Durbin-Watson: | 1.026 |
| Prob(Omnibus): | 0.456 | Jarque-Bera (JB): | 0.998 |
| Skew: | -0.312 | Prob(JB): | 0.607 |
| Kurtosis: | 1.901 | Cond. No. | 101. |
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: | 5.006 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 0.0262 |
| Time: | 21:27:06 | Log-Likelihood: | -70.750 |
| 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 | 49.4478 | 50.480 | 0.980 | 0.347 | -60.539 159.434 |
| C(dose)[T.1] | 46.7097 | 17.066 | 2.737 | 0.018 | 9.526 83.893 |
| expression | 3.7685 | 10.305 | 0.366 | 0.721 | -18.684 26.221 |
| Omnibus: | 2.062 | Durbin-Watson: | 0.723 |
| Prob(Omnibus): | 0.357 | Jarque-Bera (JB): | 1.581 |
| Skew: | -0.727 | Prob(JB): | 0.454 |
| Kurtosis: | 2.357 | Cond. No. | 35.1 |
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: | Wed, 09 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 21:27:06 | 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: | 0.115 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 1.681 |
| Date: | Wed, 09 Apr 2025 | Prob (F-statistic): | 0.217 |
| Time: | 21:27:06 | Log-Likelihood: | -74.388 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.7822 | 60.059 | 0.279 | 0.784 | -112.967 146.531 |
| expression | 15.0069 | 11.573 | 1.297 | 0.217 | -9.996 40.009 |
| Omnibus: | 0.050 | Durbin-Watson: | 1.246 |
| Prob(Omnibus): | 0.975 | Jarque-Bera (JB): | 0.213 |
| Skew: | -0.109 | Prob(JB): | 0.899 |
| Kurtosis: | 2.459 | Cond. No. | 33.8 |