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 |
| 1.136 | 0.299 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.10 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 7.20e-05 |
| Time: | 09:18:56 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.2929 | 49.503 | -0.067 | 0.948 | -106.905 100.319 |
| C(dose)[T.1] | 95.3961 | 72.501 | 1.316 | 0.204 | -56.350 247.142 |
| expression | 11.6952 | 9.994 | 1.170 | 0.256 | -9.223 32.614 |
| expression:C(dose)[T.1] | -8.6278 | 14.459 | -0.597 | 0.558 | -38.890 21.634 |
| Omnibus: | 0.587 | Durbin-Watson: | 1.936 |
| Prob(Omnibus): | 0.746 | Jarque-Bera (JB): | 0.618 |
| Skew: | -0.040 | Prob(JB): | 0.734 |
| Kurtosis: | 2.201 | Cond. No. | 112. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.11 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 1.63e-05 |
| Time: | 09:18:56 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.9762 | 35.427 | 0.479 | 0.637 | -56.923 90.876 |
| C(dose)[T.1] | 52.4463 | 8.572 | 6.118 | 0.000 | 34.566 70.327 |
| expression | 7.5727 | 7.105 | 1.066 | 0.299 | -7.248 22.393 |
| Omnibus: | 0.448 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.554 |
| Skew: | -0.060 | Prob(JB): | 0.758 |
| Kurtosis: | 2.249 | Cond. No. | 43.5 |
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: | Mon, 07 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 09:18:56 | 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: | 0.046 |
| Model: | OLS | Adj. R-squared: | 0.001 |
| Method: | Least Squares | F-statistic: | 1.020 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 0.324 |
| Time: | 09:18:56 | Log-Likelihood: | -112.56 |
| No. Observations: | 23 | AIC: | 229.1 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.9797 | 58.579 | 0.358 | 0.724 | -100.842 142.802 |
| expression | 11.8115 | 11.694 | 1.010 | 0.324 | -12.508 36.131 |
| Omnibus: | 4.128 | Durbin-Watson: | 2.561 |
| Prob(Omnibus): | 0.127 | Jarque-Bera (JB): | 1.804 |
| Skew: | 0.334 | Prob(JB): | 0.406 |
| Kurtosis: | 1.802 | Cond. No. | 43.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.978 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.350 |
| Method: | Least Squares | F-statistic: | 3.510 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 0.0528 |
| Time: | 09:18:56 | Log-Likelihood: | -70.264 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 122.3156 | 77.789 | 1.572 | 0.144 | -48.897 293.528 |
| C(dose)[T.1] | -38.2497 | 95.546 | -0.400 | 0.697 | -248.545 172.045 |
| expression | -11.4591 | 16.060 | -0.714 | 0.490 | -46.808 23.889 |
| expression:C(dose)[T.1] | 19.0385 | 20.455 | 0.931 | 0.372 | -25.982 64.059 |
| Omnibus: | 1.707 | Durbin-Watson: | 1.117 |
| Prob(Omnibus): | 0.426 | Jarque-Bera (JB): | 1.113 |
| Skew: | -0.646 | Prob(JB): | 0.573 |
| Kurtosis: | 2.668 | Cond. No. | 81.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.886 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 09:18:56 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.0974 | 48.747 | 1.356 | 0.200 | -40.113 172.308 |
| C(dose)[T.1] | 49.3337 | 16.480 | 2.994 | 0.011 | 13.426 85.241 |
| expression | 0.2779 | 9.890 | 0.028 | 0.978 | -21.271 21.827 |
| Omnibus: | 2.721 | Durbin-Watson: | 0.808 |
| Prob(Omnibus): | 0.257 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.844 | Prob(JB): | 0.393 |
| Kurtosis: | 2.626 | Cond. No. | 30.3 |
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: | Mon, 07 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 09:18:56 | 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: | 0.037 |
| Model: | OLS | Adj. R-squared: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.5023 |
| Date: | Mon, 07 Apr 2025 | Prob (F-statistic): | 0.491 |
| Time: | 09:18:56 | Log-Likelihood: | -75.016 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.1423 | 55.196 | 2.394 | 0.032 | 12.898 251.387 |
| expression | -8.5004 | 11.994 | -0.709 | 0.491 | -34.412 17.411 |
| Omnibus: | 1.484 | Durbin-Watson: | 1.412 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 0.897 |
| Skew: | 0.208 | Prob(JB): | 0.638 |
| Kurtosis: | 1.876 | Cond. No. | 26.6 |