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 |
| 2.140 | 0.159 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.729 |
| Model: | OLS | Adj. R-squared: | 0.687 |
| Method: | Least Squares | F-statistic: | 17.07 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.27e-05 |
| Time: | 22:45:16 | Log-Likelihood: | -98.075 |
| No. Observations: | 23 | AIC: | 204.2 |
| Df Residuals: | 19 | BIC: | 208.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.7946 | 84.615 | 1.061 | 0.302 | -87.307 266.896 |
| C(dose)[T.1] | -131.4158 | 106.155 | -1.238 | 0.231 | -353.600 90.769 |
| expression | -5.6796 | 13.476 | -0.421 | 0.678 | -33.886 22.527 |
| expression:C(dose)[T.1] | 31.3901 | 17.399 | 1.804 | 0.087 | -5.026 67.806 |
| Omnibus: | 0.555 | Durbin-Watson: | 1.774 |
| Prob(Omnibus): | 0.758 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.315 | Prob(JB): | 0.785 |
| Kurtosis: | 2.669 | Cond. No. | 227. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.683 |
| Model: | OLS | Adj. R-squared: | 0.651 |
| Method: | Least Squares | F-statistic: | 21.54 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.03e-05 |
| Time: | 22:45:16 | Log-Likelihood: | -99.894 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 20 | BIC: | 209.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -28.2009 | 56.632 | -0.498 | 0.624 | -146.334 89.932 |
| C(dose)[T.1] | 59.4385 | 9.321 | 6.377 | 0.000 | 39.996 78.881 |
| expression | 13.1525 | 8.992 | 1.463 | 0.159 | -5.604 31.909 |
| Omnibus: | 1.096 | Durbin-Watson: | 1.999 |
| Prob(Omnibus): | 0.578 | Jarque-Bera (JB): | 0.832 |
| Skew: | -0.109 | Prob(JB): | 0.660 |
| Kurtosis: | 2.094 | Cond. No. | 85.2 |
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: | 22:45:16 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.007 |
| Method: | Least Squares | F-statistic: | 0.8375 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.370 |
| Time: | 22:45:16 | Log-Likelihood: | -112.65 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.3110 | 82.903 | 1.873 | 0.075 | -17.096 327.718 |
| expression | -12.5076 | 13.667 | -0.915 | 0.370 | -40.930 15.914 |
| Omnibus: | 2.632 | Durbin-Watson: | 2.280 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.896 |
| Skew: | 0.527 | Prob(JB): | 0.387 |
| Kurtosis: | 2.068 | Cond. No. | 73.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.478 | 0.503 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.326 |
| Method: | Least Squares | F-statistic: | 3.256 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0635 |
| Time: | 22:45:16 | Log-Likelihood: | -70.534 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -33.4054 | 188.745 | -0.177 | 0.863 | -448.831 382.021 |
| C(dose)[T.1] | 81.5623 | 254.533 | 0.320 | 0.755 | -478.660 641.785 |
| expression | 13.0720 | 24.421 | 0.535 | 0.603 | -40.678 66.822 |
| expression:C(dose)[T.1] | -3.3431 | 34.391 | -0.097 | 0.924 | -79.037 72.351 |
| Omnibus: | 2.931 | Durbin-Watson: | 0.670 |
| Prob(Omnibus): | 0.231 | Jarque-Bera (JB): | 2.048 |
| Skew: | -0.883 | Prob(JB): | 0.359 |
| Kurtosis: | 2.598 | Cond. No. | 317. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 5.318 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0222 |
| Time: | 22:45:17 | Log-Likelihood: | -70.540 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -20.4022 | 127.544 | -0.160 | 0.876 | -298.297 257.493 |
| C(dose)[T.1] | 56.8954 | 19.033 | 2.989 | 0.011 | 15.425 98.365 |
| expression | 11.3863 | 16.470 | 0.691 | 0.503 | -24.499 47.271 |
| Omnibus: | 2.948 | Durbin-Watson: | 0.663 |
| Prob(Omnibus): | 0.229 | Jarque-Bera (JB): | 2.052 |
| Skew: | -0.884 | Prob(JB): | 0.359 |
| Kurtosis: | 2.608 | Cond. No. | 125. |
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: | 22:45:17 | 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: | 0.075 |
| Model: | OLS | Adj. R-squared: | 0.004 |
| Method: | Least Squares | F-statistic: | 1.056 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.323 |
| Time: | 22:45:17 | Log-Likelihood: | -74.714 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 221.7572 | 125.017 | 1.774 | 0.100 | -48.325 491.839 |
| expression | -17.4198 | 16.950 | -1.028 | 0.323 | -54.038 19.198 |
| Omnibus: | 0.014 | Durbin-Watson: | 1.567 |
| Prob(Omnibus): | 0.993 | Jarque-Bera (JB): | 0.230 |
| Skew: | 0.003 | Prob(JB): | 0.891 |
| Kurtosis: | 2.394 | Cond. No. | 96.1 |