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 |
| 0.014 | 0.907 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.15 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000114 |
| Time: | 23:02:20 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.8483 | 89.303 | 1.084 | 0.292 | -90.064 283.761 |
| C(dose)[T.1] | -49.1732 | 152.561 | -0.322 | 0.751 | -368.486 270.140 |
| expression | -5.9655 | 12.464 | -0.479 | 0.638 | -32.053 20.122 |
| expression:C(dose)[T.1] | 14.6614 | 21.846 | 0.671 | 0.510 | -31.063 60.386 |
| Omnibus: | 1.567 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.457 | Jarque-Bera (JB): | 1.022 |
| Skew: | 0.177 | Prob(JB): | 0.600 |
| Kurtosis: | 2.030 | Cond. No. | 297. |
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: | 18.51 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.81e-05 |
| Time: | 23:02:20 | 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 | 62.7358 | 72.410 | 0.866 | 0.397 | -88.308 213.780 |
| C(dose)[T.1] | 53.0234 | 9.160 | 5.789 | 0.000 | 33.916 72.131 |
| expression | -1.1930 | 10.095 | -0.118 | 0.907 | -22.250 19.864 |
| Omnibus: | 0.243 | Durbin-Watson: | 1.889 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.436 |
| Skew: | 0.051 | Prob(JB): | 0.804 |
| Kurtosis: | 2.334 | Cond. No. | 119. |
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: | 23:02:20 | 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: | 0.062 |
| Model: | OLS | Adj. R-squared: | 0.017 |
| Method: | Least Squares | F-statistic: | 1.382 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.253 |
| Time: | 23:02:20 | Log-Likelihood: | -112.37 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 231.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 207.0233 | 108.519 | 1.908 | 0.070 | -18.655 432.702 |
| expression | -18.1295 | 15.422 | -1.176 | 0.253 | -50.201 13.942 |
| Omnibus: | 3.620 | Durbin-Watson: | 2.524 |
| Prob(Omnibus): | 0.164 | Jarque-Bera (JB): | 1.709 |
| Skew: | 0.334 | Prob(JB): | 0.425 |
| Kurtosis: | 1.844 | Cond. No. | 111. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.605 | 0.229 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 3.913 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0399 |
| Time: | 23:02:20 | Log-Likelihood: | -69.853 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -169.9484 | 246.222 | -0.690 | 0.504 | -711.880 371.983 |
| C(dose)[T.1] | 118.2600 | 328.166 | 0.360 | 0.725 | -604.029 840.549 |
| expression | 31.3318 | 32.465 | 0.965 | 0.355 | -40.124 102.788 |
| expression:C(dose)[T.1] | -10.0274 | 42.501 | -0.236 | 0.818 | -103.571 83.516 |
| Omnibus: | 2.037 | Durbin-Watson: | 1.071 |
| Prob(Omnibus): | 0.361 | Jarque-Bera (JB): | 1.478 |
| Skew: | -0.594 | Prob(JB): | 0.478 |
| Kurtosis: | 2.023 | Cond. No. | 465. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.433 |
| Method: | Least Squares | F-statistic: | 6.341 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0132 |
| Time: | 23:02:20 | Log-Likelihood: | -69.891 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -125.6195 | 152.745 | -0.822 | 0.427 | -458.422 207.183 |
| C(dose)[T.1] | 40.9364 | 16.156 | 2.534 | 0.026 | 5.736 76.137 |
| expression | 25.4807 | 20.111 | 1.267 | 0.229 | -18.337 69.298 |
| Omnibus: | 1.970 | Durbin-Watson: | 1.017 |
| Prob(Omnibus): | 0.374 | Jarque-Bera (JB): | 1.488 |
| Skew: | -0.621 | Prob(JB): | 0.475 |
| Kurtosis: | 2.085 | Cond. No. | 164. |
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: | 23:02:21 | 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: | 0.254 |
| Model: | OLS | Adj. R-squared: | 0.196 |
| Method: | Least Squares | F-statistic: | 4.419 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0556 |
| Time: | 23:02:21 | Log-Likelihood: | -73.106 |
| No. Observations: | 15 | AIC: | 150.2 |
| Df Residuals: | 13 | BIC: | 151.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -263.1302 | 169.959 | -1.548 | 0.146 | -630.305 104.044 |
| expression | 46.0435 | 21.903 | 2.102 | 0.056 | -1.276 93.363 |
| Omnibus: | 1.100 | Durbin-Watson: | 1.708 |
| Prob(Omnibus): | 0.577 | Jarque-Bera (JB): | 0.751 |
| Skew: | 0.108 | Prob(JB): | 0.687 |
| Kurtosis: | 1.925 | Cond. No. | 153. |