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 |
| 0.599 | 0.448 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.73 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.59e-05 |
| Time: | 22:52:12 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.3045 | 108.213 | 0.326 | 0.748 | -191.188 261.797 |
| C(dose)[T.1] | 141.6525 | 128.015 | 1.107 | 0.282 | -126.287 409.592 |
| expression | 2.5489 | 14.568 | 0.175 | 0.863 | -27.942 33.040 |
| expression:C(dose)[T.1] | -12.0542 | 17.297 | -0.697 | 0.494 | -48.258 24.150 |
| Omnibus: | 0.182 | Durbin-Watson: | 2.020 |
| Prob(Omnibus): | 0.913 | Jarque-Bera (JB): | 0.172 |
| Skew: | 0.161 | Prob(JB): | 0.918 |
| Kurtosis: | 2.724 | Cond. No. | 313. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.35 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.11e-05 |
| Time: | 22:52:12 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 98.7171 | 57.808 | 1.708 | 0.103 | -21.868 219.302 |
| C(dose)[T.1] | 52.6525 | 8.686 | 6.061 | 0.000 | 34.533 70.772 |
| expression | -6.0013 | 7.753 | -0.774 | 0.448 | -22.173 10.171 |
| Omnibus: | 0.110 | Durbin-Watson: | 2.014 |
| Prob(Omnibus): | 0.946 | Jarque-Bera (JB): | 0.255 |
| Skew: | 0.139 | Prob(JB): | 0.880 |
| Kurtosis: | 2.565 | Cond. No. | 101. |
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: | 22:52:12 | 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: | 0.033 |
| Model: | OLS | Adj. R-squared: | -0.013 |
| Method: | Least Squares | F-statistic: | 0.7239 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.404 |
| Time: | 22:52:12 | Log-Likelihood: | -112.71 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.1230 | 93.599 | 1.700 | 0.104 | -35.528 353.774 |
| expression | -10.7860 | 12.677 | -0.851 | 0.404 | -37.150 15.578 |
| Omnibus: | 2.053 | Durbin-Watson: | 2.598 |
| Prob(Omnibus): | 0.358 | Jarque-Bera (JB): | 1.225 |
| Skew: | 0.247 | Prob(JB): | 0.542 |
| Kurtosis: | 1.983 | Cond. No. | 99.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.088 | 0.771 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.490 |
| Model: | OLS | Adj. R-squared: | 0.351 |
| Method: | Least Squares | F-statistic: | 3.519 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0525 |
| Time: | 22:52:12 | Log-Likelihood: | -70.255 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 196.7251 | 140.615 | 1.399 | 0.189 | -112.766 506.216 |
| C(dose)[T.1] | -94.1815 | 162.286 | -0.580 | 0.573 | -451.371 263.008 |
| expression | -23.0791 | 25.015 | -0.923 | 0.376 | -78.136 31.978 |
| expression:C(dose)[T.1] | 25.5158 | 28.615 | 0.892 | 0.392 | -37.464 88.496 |
| Omnibus: | 1.877 | Durbin-Watson: | 0.758 |
| Prob(Omnibus): | 0.391 | Jarque-Bera (JB): | 1.300 |
| Skew: | -0.691 | Prob(JB): | 0.522 |
| Kurtosis: | 2.589 | Cond. No. | 182. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 4.965 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0268 |
| Time: | 22:52:13 | Log-Likelihood: | -70.778 |
| 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 | 87.4831 | 68.431 | 1.278 | 0.225 | -61.615 236.582 |
| C(dose)[T.1] | 49.8285 | 15.826 | 3.149 | 0.008 | 15.348 84.309 |
| expression | -3.5797 | 12.043 | -0.297 | 0.771 | -29.818 22.659 |
| Omnibus: | 2.557 | Durbin-Watson: | 0.832 |
| Prob(Omnibus): | 0.278 | Jarque-Bera (JB): | 1.743 |
| Skew: | -0.814 | Prob(JB): | 0.418 |
| Kurtosis: | 2.632 | Cond. No. | 51.9 |
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: | 22:52:13 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.009560 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.924 |
| Time: | 22:52:13 | Log-Likelihood: | -75.295 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.0374 | 88.841 | 0.957 | 0.356 | -106.892 276.966 |
| expression | 1.5148 | 15.493 | 0.098 | 0.924 | -31.957 34.986 |
| Omnibus: | 0.488 | Durbin-Watson: | 1.603 |
| Prob(Omnibus): | 0.784 | Jarque-Bera (JB): | 0.535 |
| Skew: | 0.026 | Prob(JB): | 0.765 |
| Kurtosis: | 2.076 | Cond. No. | 51.7 |