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 |
| 0.908 | 0.352 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.678 |
| Method: | Least Squares | F-statistic: | 16.41 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.66e-05 |
| Time: | 22:45:15 | Log-Likelihood: | -98.400 |
| No. Observations: | 23 | AIC: | 204.8 |
| Df Residuals: | 19 | BIC: | 209.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 116.9618 | 71.408 | 1.638 | 0.118 | -32.496 266.419 |
| C(dose)[T.1] | -125.9268 | 91.572 | -1.375 | 0.185 | -317.589 65.736 |
| expression | -9.2983 | 10.549 | -0.881 | 0.389 | -31.377 12.780 |
| expression:C(dose)[T.1] | 26.9877 | 13.648 | 1.977 | 0.063 | -1.577 55.552 |
| Omnibus: | 2.663 | Durbin-Watson: | 1.871 |
| Prob(Omnibus): | 0.264 | Jarque-Bera (JB): | 1.343 |
| Skew: | 0.222 | Prob(JB): | 0.511 |
| Kurtosis: | 1.903 | Cond. No. | 212. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 19.79 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.82e-05 |
| Time: | 22:45:15 | Log-Likelihood: | -100.55 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 8.1487 | 48.708 | 0.167 | 0.869 | -93.455 109.752 |
| C(dose)[T.1] | 54.4456 | 8.656 | 6.290 | 0.000 | 36.390 72.501 |
| expression | 6.8248 | 7.163 | 0.953 | 0.352 | -8.118 21.767 |
| Omnibus: | 0.744 | Durbin-Watson: | 2.121 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.774 |
| Skew: | 0.271 | Prob(JB): | 0.679 |
| Kurtosis: | 2.283 | Cond. No. | 78.0 |
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: | 22:45:15 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004132 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.949 |
| Time: | 22:45:15 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.5907 | 80.080 | 0.931 | 0.362 | -91.944 241.125 |
| expression | 0.7685 | 11.955 | 0.064 | 0.949 | -24.093 25.630 |
| Omnibus: | 3.314 | Durbin-Watson: | 2.493 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.553 |
| Skew: | 0.277 | Prob(JB): | 0.460 |
| Kurtosis: | 1.854 | Cond. No. | 76.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.076 | 0.787 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.515 |
| Model: | OLS | Adj. R-squared: | 0.383 |
| Method: | Least Squares | F-statistic: | 3.896 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0404 |
| Time: | 22:45:15 | Log-Likelihood: | -69.871 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.7454 | 102.483 | 0.222 | 0.828 | -202.817 248.308 |
| C(dose)[T.1] | 253.5347 | 173.274 | 1.463 | 0.171 | -127.838 634.908 |
| expression | 6.7594 | 15.409 | 0.439 | 0.669 | -27.156 40.675 |
| expression:C(dose)[T.1] | -32.7549 | 27.423 | -1.194 | 0.257 | -93.113 27.603 |
| Omnibus: | 0.945 | Durbin-Watson: | 0.787 |
| Prob(Omnibus): | 0.623 | Jarque-Bera (JB): | 0.851 |
| Skew: | -0.402 | Prob(JB): | 0.653 |
| Kurtosis: | 2.155 | Cond. No. | 181. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.361 |
| Method: | Least Squares | F-statistic: | 4.954 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0270 |
| Time: | 22:45:15 | Log-Likelihood: | -70.785 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.1104 | 86.508 | 1.053 | 0.313 | -97.374 279.595 |
| C(dose)[T.1] | 47.5168 | 16.827 | 2.824 | 0.015 | 10.854 84.180 |
| expression | -3.5825 | 12.971 | -0.276 | 0.787 | -31.844 24.679 |
| Omnibus: | 2.316 | Durbin-Watson: | 0.791 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.678 |
| Skew: | -0.781 | Prob(JB): | 0.432 |
| Kurtosis: | 2.503 | Cond. No. | 72.8 |
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: | 22:45:15 | 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: | 0.088 |
| Model: | OLS | Adj. R-squared: | 0.018 |
| Method: | Least Squares | F-statistic: | 1.259 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.282 |
| Time: | 22:45:15 | Log-Likelihood: | -74.607 |
| No. Observations: | 15 | AIC: | 153.2 |
| Df Residuals: | 13 | BIC: | 154.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 200.6482 | 95.845 | 2.093 | 0.056 | -6.412 407.709 |
| expression | -16.8198 | 14.991 | -1.122 | 0.282 | -49.207 15.567 |
| Omnibus: | 0.201 | Durbin-Watson: | 1.361 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.396 |
| Skew: | -0.106 | Prob(JB): | 0.821 |
| Kurtosis: | 2.233 | Cond. No. | 64.7 |