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 |
| 1.727 | 0.204 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.709 |
| Model: | OLS | Adj. R-squared: | 0.663 |
| Method: | Least Squares | F-statistic: | 15.40 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 2.54e-05 |
| Time: | 11:46:49 | Log-Likelihood: | -98.925 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 226.8628 | 89.459 | 2.536 | 0.020 | 39.623 414.103 |
| C(dose)[T.1] | -99.6849 | 103.692 | -0.961 | 0.348 | -316.714 117.344 |
| expression | -27.4077 | 14.172 | -1.934 | 0.068 | -57.071 2.256 |
| expression:C(dose)[T.1] | 24.0480 | 16.743 | 1.436 | 0.167 | -10.996 59.092 |
| Omnibus: | 0.289 | Durbin-Watson: | 2.254 |
| Prob(Omnibus): | 0.865 | Jarque-Bera (JB): | 0.247 |
| Skew: | -0.213 | Prob(JB): | 0.884 |
| Kurtosis: | 2.723 | Cond. No. | 226. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 20.96 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.24e-05 |
| Time: | 11:46:49 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 118.3213 | 49.130 | 2.408 | 0.026 | 15.838 220.804 |
| C(dose)[T.1] | 48.6968 | 9.125 | 5.337 | 0.000 | 29.663 67.731 |
| expression | -10.1775 | 7.744 | -1.314 | 0.204 | -26.331 5.976 |
| Omnibus: | 0.973 | Durbin-Watson: | 2.037 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.171 |
| Skew: | -0.144 | Prob(JB): | 0.918 |
| Kurtosis: | 3.309 | Cond. No. | 73.7 |
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, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:46:49 | 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: | 0.217 |
| Model: | OLS | Adj. R-squared: | 0.180 |
| Method: | Least Squares | F-statistic: | 5.817 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0251 |
| Time: | 11:46:49 | Log-Likelihood: | -110.29 |
| No. Observations: | 23 | AIC: | 224.6 |
| Df Residuals: | 21 | BIC: | 226.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 238.8640 | 66.291 | 3.603 | 0.002 | 101.004 376.724 |
| expression | -26.1693 | 10.850 | -2.412 | 0.025 | -48.733 -3.606 |
| Omnibus: | 1.761 | Durbin-Watson: | 2.715 |
| Prob(Omnibus): | 0.414 | Jarque-Bera (JB): | 1.417 |
| Skew: | 0.580 | Prob(JB): | 0.492 |
| Kurtosis: | 2.637 | Cond. No. | 65.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.361 | 0.150 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.540 |
| Model: | OLS | Adj. R-squared: | 0.415 |
| Method: | Least Squares | F-statistic: | 4.310 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0307 |
| Time: | 11:46:49 | Log-Likelihood: | -69.470 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 275.7585 | 260.543 | 1.058 | 0.313 | -297.693 849.210 |
| C(dose)[T.1] | 10.7176 | 294.258 | 0.036 | 0.972 | -636.939 658.375 |
| expression | -30.7475 | 38.420 | -0.800 | 0.440 | -115.308 53.813 |
| expression:C(dose)[T.1] | 6.5202 | 43.064 | 0.151 | 0.882 | -88.263 101.303 |
| Omnibus: | 2.464 | Durbin-Watson: | 0.851 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.233 |
| Skew: | -0.340 | Prob(JB): | 0.540 |
| Kurtosis: | 1.772 | Cond. No. | 421. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.539 |
| Model: | OLS | Adj. R-squared: | 0.463 |
| Method: | Least Squares | F-statistic: | 7.026 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00955 |
| Time: | 11:46:49 | Log-Likelihood: | -69.486 |
| No. Observations: | 15 | AIC: | 145.0 |
| Df Residuals: | 12 | BIC: | 147.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 240.5957 | 113.190 | 2.126 | 0.055 | -6.025 487.216 |
| C(dose)[T.1] | 55.2082 | 14.910 | 3.703 | 0.003 | 22.721 87.695 |
| expression | -25.5578 | 16.634 | -1.537 | 0.150 | -61.799 10.684 |
| Omnibus: | 2.217 | Durbin-Watson: | 0.817 |
| Prob(Omnibus): | 0.330 | Jarque-Bera (JB): | 1.191 |
| Skew: | -0.350 | Prob(JB): | 0.551 |
| Kurtosis: | 1.809 | Cond. No. | 112. |
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, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:46:49 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.063 |
| Method: | Least Squares | F-statistic: | 0.1733 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.684 |
| Time: | 11:46:49 | Log-Likelihood: | -75.201 |
| 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 | 158.5101 | 156.096 | 1.015 | 0.328 | -178.716 495.736 |
| expression | -9.3963 | 22.572 | -0.416 | 0.684 | -58.160 39.368 |
| Omnibus: | 0.655 | Durbin-Watson: | 1.646 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.612 |
| Skew: | 0.119 | Prob(JB): | 0.736 |
| Kurtosis: | 2.040 | Cond. No. | 109. |