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 |
| 3.370 | 0.081 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.731 |
| Model: | OLS | Adj. R-squared: | 0.688 |
| Method: | Least Squares | F-statistic: | 17.20 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.21e-05 |
| Time: | 22:45:56 | Log-Likelihood: | -98.010 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 105.7518 | 86.040 | 1.229 | 0.234 | -74.333 285.836 |
| C(dose)[T.1] | 242.8087 | 134.768 | 1.802 | 0.087 | -39.265 524.882 |
| expression | -7.4857 | 12.471 | -0.600 | 0.555 | -33.587 18.616 |
| expression:C(dose)[T.1] | -30.4700 | 20.532 | -1.484 | 0.154 | -73.444 12.504 |
| Omnibus: | 1.906 | Durbin-Watson: | 2.069 |
| Prob(Omnibus): | 0.386 | Jarque-Bera (JB): | 1.536 |
| Skew: | 0.479 | Prob(JB): | 0.464 |
| Kurtosis: | 2.172 | Cond. No. | 285. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.670 |
| Method: | Least Squares | F-statistic: | 23.30 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.97e-06 |
| Time: | 22:45:56 | Log-Likelihood: | -99.272 |
| No. Observations: | 23 | AIC: | 204.5 |
| Df Residuals: | 20 | BIC: | 207.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.1499 | 70.459 | 2.599 | 0.017 | 36.176 330.124 |
| C(dose)[T.1] | 43.3062 | 9.781 | 4.427 | 0.000 | 22.903 63.709 |
| expression | -18.7264 | 10.200 | -1.836 | 0.081 | -40.004 2.551 |
| Omnibus: | 2.340 | Durbin-Watson: | 2.102 |
| Prob(Omnibus): | 0.310 | Jarque-Bera (JB): | 1.555 |
| Skew: | 0.409 | Prob(JB): | 0.460 |
| Kurtosis: | 2.023 | 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: | 22:45:56 | 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: | 0.405 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 14.31 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00109 |
| Time: | 22:45:57 | Log-Likelihood: | -107.13 |
| No. Observations: | 23 | AIC: | 218.3 |
| Df Residuals: | 21 | BIC: | 220.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 371.1052 | 77.223 | 4.806 | 0.000 | 210.510 531.700 |
| expression | -43.9541 | 11.618 | -3.783 | 0.001 | -68.116 -19.792 |
| Omnibus: | 1.644 | Durbin-Watson: | 3.042 |
| Prob(Omnibus): | 0.440 | Jarque-Bera (JB): | 0.977 |
| Skew: | -0.047 | Prob(JB): | 0.613 |
| Kurtosis: | 1.995 | Cond. No. | 94.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.749 | 0.050 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.614 |
| Model: | OLS | Adj. R-squared: | 0.508 |
| Method: | Least Squares | F-statistic: | 5.820 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0124 |
| Time: | 22:45:57 | Log-Likelihood: | -68.170 |
| No. Observations: | 15 | AIC: | 144.3 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.0895 | 164.327 | 1.425 | 0.182 | -127.592 595.771 |
| C(dose)[T.1] | 176.9858 | 225.406 | 0.785 | 0.449 | -319.129 673.101 |
| expression | -25.7580 | 25.350 | -1.016 | 0.331 | -81.552 30.036 |
| expression:C(dose)[T.1] | -16.4644 | 33.619 | -0.490 | 0.634 | -90.460 57.531 |
| Omnibus: | 1.321 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.517 | Jarque-Bera (JB): | 1.024 |
| Skew: | -0.422 | Prob(JB): | 0.599 |
| Kurtosis: | 2.037 | Cond. No. | 311. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.605 |
| Model: | OLS | Adj. R-squared: | 0.539 |
| Method: | Least Squares | F-statistic: | 9.193 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00379 |
| Time: | 22:45:57 | Log-Likelihood: | -68.332 |
| No. Observations: | 15 | AIC: | 142.7 |
| Df Residuals: | 12 | BIC: | 144.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 294.6570 | 104.720 | 2.814 | 0.016 | 66.491 522.823 |
| C(dose)[T.1] | 66.8799 | 15.599 | 4.287 | 0.001 | 32.892 100.868 |
| expression | -35.1189 | 16.115 | -2.179 | 0.050 | -70.230 -0.008 |
| Omnibus: | 1.432 | Durbin-Watson: | 1.829 |
| Prob(Omnibus): | 0.489 | Jarque-Bera (JB): | 1.074 |
| Skew: | -0.432 | Prob(JB): | 0.585 |
| Kurtosis: | 2.014 | Cond. No. | 109. |
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: | 22:45:57 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.001522 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.969 |
| Time: | 22:45:57 | Log-Likelihood: | -75.299 |
| 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 | 88.1353 | 142.149 | 0.620 | 0.546 | -218.960 395.230 |
| expression | 0.8208 | 21.040 | 0.039 | 0.969 | -44.634 46.275 |
| Omnibus: | 0.582 | Durbin-Watson: | 1.595 |
| Prob(Omnibus): | 0.748 | Jarque-Bera (JB): | 0.573 |
| Skew: | 0.041 | Prob(JB): | 0.751 |
| Kurtosis: | 2.046 | Cond. No. | 96.6 |