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 |
| 0.379 | 0.545 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.732 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 17.27 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 1.18e-05 |
| Time: | 06:46:53 | Log-Likelihood: | -97.977 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.7879 | 64.476 | 1.858 | 0.079 | -15.163 254.738 |
| C(dose)[T.1] | -164.6831 | 94.999 | -1.734 | 0.099 | -363.518 34.152 |
| expression | -10.1048 | 9.899 | -1.021 | 0.320 | -30.824 10.615 |
| expression:C(dose)[T.1] | 34.8074 | 14.999 | 2.321 | 0.032 | 3.414 66.201 |
| Omnibus: | 0.246 | Durbin-Watson: | 1.821 |
| Prob(Omnibus): | 0.884 | Jarque-Bera (JB): | 0.040 |
| Skew: | -0.090 | Prob(JB): | 0.980 |
| Kurtosis: | 2.904 | Cond. No. | 199. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.03 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 2.35e-05 |
| Time: | 06:46:53 | Log-Likelihood: | -100.85 |
| 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 | 21.3858 | 53.633 | 0.399 | 0.694 | -90.491 133.263 |
| C(dose)[T.1] | 54.9501 | 9.074 | 6.056 | 0.000 | 36.022 73.878 |
| expression | 5.0574 | 8.212 | 0.616 | 0.545 | -12.073 22.187 |
| Omnibus: | 0.112 | Durbin-Watson: | 1.842 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.337 |
| Skew: | -0.012 | Prob(JB): | 0.845 |
| Kurtosis: | 2.408 | Cond. No. | 80.9 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 06:46:53 | 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: | 0.024 |
| Model: | OLS | Adj. R-squared: | -0.022 |
| Method: | Least Squares | F-statistic: | 0.5180 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.480 |
| Time: | 06:46:53 | Log-Likelihood: | -112.82 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.6311 | 82.165 | 1.687 | 0.106 | -32.241 309.504 |
| expression | -9.2961 | 12.916 | -0.720 | 0.480 | -36.157 17.565 |
| Omnibus: | 2.070 | Durbin-Watson: | 2.495 |
| Prob(Omnibus): | 0.355 | Jarque-Bera (JB): | 1.633 |
| Skew: | 0.497 | Prob(JB): | 0.442 |
| Kurtosis: | 2.153 | Cond. No. | 75.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.007 | 0.936 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.299 |
| Method: | Least Squares | F-statistic: | 2.989 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0775 |
| Time: | 06:46:53 | Log-Likelihood: | -70.829 |
| No. Observations: | 15 | AIC: | 149.7 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.5555 | 61.630 | 1.145 | 0.277 | -65.092 206.203 |
| C(dose)[T.1] | 49.0841 | 80.573 | 0.609 | 0.555 | -128.256 226.425 |
| expression | -0.6337 | 12.252 | -0.052 | 0.960 | -27.599 26.332 |
| expression:C(dose)[T.1] | -0.0202 | 16.460 | -0.001 | 0.999 | -36.248 36.207 |
| Omnibus: | 2.484 | Durbin-Watson: | 0.822 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.747 |
| Skew: | -0.808 | Prob(JB): | 0.418 |
| Kurtosis: | 2.569 | Cond. No. | 67.7 |
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: | 4.891 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 06:46:53 | Log-Likelihood: | -70.829 |
| 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 | 70.6107 | 40.322 | 1.751 | 0.105 | -17.244 158.466 |
| C(dose)[T.1] | 48.9874 | 15.939 | 3.073 | 0.010 | 14.260 83.715 |
| expression | -0.6449 | 7.833 | -0.082 | 0.936 | -17.712 16.422 |
| Omnibus: | 2.485 | Durbin-Watson: | 0.822 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.747 |
| Skew: | -0.808 | Prob(JB): | 0.417 |
| Kurtosis: | 2.569 | Cond. No. | 26.4 |
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: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 06:46:53 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.060 |
| Method: | Least Squares | F-statistic: | 0.2034 |
| Date: | Tue, 04 Nov 2025 | Prob (F-statistic): | 0.659 |
| Time: | 06:46:53 | Log-Likelihood: | -75.184 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.9953 | 48.355 | 2.378 | 0.033 | 10.530 219.461 |
| expression | -4.4796 | 9.933 | -0.451 | 0.659 | -25.938 16.979 |
| Omnibus: | 0.659 | Durbin-Watson: | 1.674 |
| Prob(Omnibus): | 0.719 | Jarque-Bera (JB): | 0.622 |
| Skew: | 0.151 | Prob(JB): | 0.733 |
| Kurtosis: | 2.049 | Cond. No. | 24.3 |