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 |
| 1.176 | 0.291 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 12.80 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 8.32e-05 |
| Time: | 11:40:03 | Log-Likelihood: | -100.39 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 99.9059 | 63.982 | 1.561 | 0.135 | -34.010 233.822 |
| C(dose)[T.1] | 65.0557 | 97.010 | 0.671 | 0.511 | -137.988 268.100 |
| expression | -6.8738 | 9.581 | -0.717 | 0.482 | -26.927 13.180 |
| expression:C(dose)[T.1] | -2.1766 | 14.931 | -0.146 | 0.886 | -33.427 29.074 |
| Omnibus: | 0.149 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.928 | Jarque-Bera (JB): | 0.364 |
| Skew: | 0.073 | Prob(JB): | 0.834 |
| Kurtosis: | 2.401 | Cond. No. | 185. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.17 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.60e-05 |
| Time: | 11:40:03 | Log-Likelihood: | -100.41 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 105.8645 | 48.004 | 2.205 | 0.039 | 5.730 205.999 |
| C(dose)[T.1] | 50.9750 | 8.797 | 5.795 | 0.000 | 32.625 69.325 |
| expression | -7.7701 | 7.166 | -1.084 | 0.291 | -22.719 7.178 |
| Omnibus: | 0.190 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.909 | Jarque-Bera (JB): | 0.397 |
| Skew: | 0.080 | Prob(JB): | 0.820 |
| Kurtosis: | 2.377 | Cond. No. | 75.6 |
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: | 11:40:03 | 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: | 0.112 |
| Model: | OLS | Adj. R-squared: | 0.070 |
| Method: | Least Squares | F-statistic: | 2.650 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.118 |
| Time: | 11:40:03 | Log-Likelihood: | -111.74 |
| No. Observations: | 23 | AIC: | 227.5 |
| Df Residuals: | 21 | BIC: | 229.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 197.1130 | 72.434 | 2.721 | 0.013 | 46.479 347.747 |
| expression | -18.0535 | 11.090 | -1.628 | 0.118 | -41.116 5.009 |
| Omnibus: | 3.444 | Durbin-Watson: | 2.057 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 1.604 |
| Skew: | 0.294 | Prob(JB): | 0.448 |
| Kurtosis: | 1.847 | Cond. No. | 71.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.624 | 0.445 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.345 |
| Method: | Least Squares | F-statistic: | 3.459 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0548 |
| Time: | 11:40:03 | Log-Likelihood: | -70.317 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -147.7073 | 320.924 | -0.460 | 0.654 | -854.055 558.641 |
| C(dose)[T.1] | 204.5929 | 337.341 | 0.606 | 0.557 | -537.889 947.074 |
| expression | 33.0112 | 49.211 | 0.671 | 0.516 | -75.302 141.325 |
| expression:C(dose)[T.1] | -23.3354 | 51.982 | -0.449 | 0.662 | -137.748 91.077 |
| Omnibus: | 3.094 | Durbin-Watson: | 0.874 |
| Prob(Omnibus): | 0.213 | Jarque-Bera (JB): | 1.956 |
| Skew: | -0.879 | Prob(JB): | 0.376 |
| Kurtosis: | 2.808 | Cond. No. | 434. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.389 |
| Method: | Least Squares | F-statistic: | 5.451 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0207 |
| Time: | 11:40:03 | Log-Likelihood: | -70.453 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -11.4108 | 100.444 | -0.114 | 0.911 | -230.260 207.438 |
| C(dose)[T.1] | 53.3456 | 16.220 | 3.289 | 0.006 | 18.005 88.686 |
| expression | 12.0974 | 15.316 | 0.790 | 0.445 | -21.274 45.469 |
| Omnibus: | 3.591 | Durbin-Watson: | 0.797 |
| Prob(Omnibus): | 0.166 | Jarque-Bera (JB): | 2.177 |
| Skew: | -0.933 | Prob(JB): | 0.337 |
| Kurtosis: | 2.941 | Cond. No. | 85.8 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:40:03 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04826 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.830 |
| Time: | 11:40:03 | Log-Likelihood: | -75.272 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.3785 | 122.022 | 0.987 | 0.342 | -143.234 383.991 |
| expression | -4.2171 | 19.198 | -0.220 | 0.830 | -45.691 37.257 |
| Omnibus: | 0.397 | Durbin-Watson: | 1.584 |
| Prob(Omnibus): | 0.820 | Jarque-Bera (JB): | 0.499 |
| Skew: | 0.068 | Prob(JB): | 0.779 |
| Kurtosis: | 2.117 | Cond. No. | 78.3 |