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 |
| 1.125 | 0.302 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.686 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.84 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 5.06e-05 |
| Time: | 11:49:43 | Log-Likelihood: | -99.779 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.4728 | 88.720 | 0.287 | 0.777 | -160.221 211.166 |
| C(dose)[T.1] | -122.1623 | 165.577 | -0.738 | 0.470 | -468.720 224.395 |
| expression | 3.6399 | 11.213 | 0.325 | 0.749 | -19.830 27.110 |
| expression:C(dose)[T.1] | 21.8819 | 20.745 | 1.055 | 0.305 | -21.538 65.302 |
| Omnibus: | 2.158 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.340 | Jarque-Bera (JB): | 1.450 |
| Skew: | 0.380 | Prob(JB): | 0.484 |
| Kurtosis: | 2.033 | Cond. No. | 375. |
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: | 20.10 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.64e-05 |
| Time: | 11:49:43 | 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 | -25.0005 | 74.920 | -0.334 | 0.742 | -181.281 131.280 |
| C(dose)[T.1] | 52.2555 | 8.594 | 6.081 | 0.000 | 34.329 70.182 |
| expression | 10.0333 | 9.461 | 1.061 | 0.302 | -9.701 29.768 |
| Omnibus: | 2.083 | Durbin-Watson: | 1.903 |
| Prob(Omnibus): | 0.353 | Jarque-Bera (JB): | 1.137 |
| Skew: | 0.146 | Prob(JB): | 0.566 |
| Kurtosis: | 1.951 | Cond. No. | 142. |
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: | 11:49:43 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | 0.008 |
| Method: | Least Squares | F-statistic: | 1.187 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.288 |
| Time: | 11:49:43 | Log-Likelihood: | -112.47 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -54.2558 | 123.147 | -0.441 | 0.664 | -310.354 201.843 |
| expression | 16.8602 | 15.473 | 1.090 | 0.288 | -15.317 49.037 |
| Omnibus: | 1.671 | Durbin-Watson: | 2.590 |
| Prob(Omnibus): | 0.434 | Jarque-Bera (JB): | 1.078 |
| Skew: | 0.208 | Prob(JB): | 0.583 |
| Kurtosis: | 2.024 | Cond. No. | 142. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.993 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.300 |
| Method: | Least Squares | F-statistic: | 2.999 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0769 |
| Time: | 11:49:43 | Log-Likelihood: | -70.817 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 40.3046 | 239.517 | 0.168 | 0.869 | -486.868 567.477 |
| C(dose)[T.1] | 95.1643 | 302.233 | 0.315 | 0.759 | -570.046 760.374 |
| expression | 3.2934 | 29.046 | 0.113 | 0.912 | -60.636 67.223 |
| expression:C(dose)[T.1] | -5.6734 | 37.197 | -0.153 | 0.882 | -87.544 76.197 |
| Omnibus: | 2.496 | Durbin-Watson: | 0.804 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.736 |
| Skew: | -0.808 | Prob(JB): | 0.420 |
| Kurtosis: | 2.591 | Cond. No. | 422. |
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: | 4.885 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0281 |
| Time: | 11:49:43 | 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 | 68.7948 | 143.689 | 0.479 | 0.641 | -244.277 381.867 |
| C(dose)[T.1] | 49.1437 | 16.684 | 2.946 | 0.012 | 12.793 85.494 |
| expression | -0.1659 | 17.391 | -0.010 | 0.993 | -38.058 37.726 |
| Omnibus: | 2.692 | Durbin-Watson: | 0.812 |
| Prob(Omnibus): | 0.260 | Jarque-Bera (JB): | 1.856 |
| Skew: | -0.840 | Prob(JB): | 0.395 |
| Kurtosis: | 2.615 | Cond. No. | 151. |
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: | 11:49:43 | 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: | 0.050 |
| Model: | OLS | Adj. R-squared: | -0.023 |
| Method: | Least Squares | F-statistic: | 0.6873 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.422 |
| Time: | 11:49:43 | Log-Likelihood: | -74.914 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 232.0340 | 167.194 | 1.388 | 0.189 | -129.167 593.235 |
| expression | -17.1541 | 20.692 | -0.829 | 0.422 | -61.855 27.547 |
| Omnibus: | 0.169 | Durbin-Watson: | 1.452 |
| Prob(Omnibus): | 0.919 | Jarque-Bera (JB): | 0.337 |
| Skew: | 0.186 | Prob(JB): | 0.845 |
| Kurtosis: | 2.368 | Cond. No. | 139. |