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 |
| 0.782 | 0.387 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 13.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.64e-05 |
| Time: | 06:26:38 | Log-Likelihood: | -100.11 |
| No. Observations: | 23 | AIC: | 208.2 |
| Df Residuals: | 19 | BIC: | 212.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.3230 | 56.195 | 0.789 | 0.440 | -73.293 161.939 |
| C(dose)[T.1] | -32.5433 | 94.446 | -0.345 | 0.734 | -230.222 165.135 |
| expression | 1.8485 | 10.449 | 0.177 | 0.861 | -20.021 23.718 |
| expression:C(dose)[T.1] | 16.6184 | 17.944 | 0.926 | 0.366 | -20.940 54.176 |
| Omnibus: | 0.654 | Durbin-Watson: | 2.195 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.685 |
| Skew: | 0.337 | Prob(JB): | 0.710 |
| Kurtosis: | 2.490 | Cond. No. | 145. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.93e-05 |
| Time: | 06:26:38 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 14.1914 | 45.654 | 0.311 | 0.759 | -81.041 109.424 |
| C(dose)[T.1] | 54.5483 | 8.712 | 6.261 | 0.000 | 36.376 72.721 |
| expression | 7.4831 | 8.464 | 0.884 | 0.387 | -10.173 25.140 |
| Omnibus: | 0.365 | Durbin-Watson: | 2.218 |
| Prob(Omnibus): | 0.833 | Jarque-Bera (JB): | 0.498 |
| Skew: | 0.232 | Prob(JB): | 0.779 |
| Kurtosis: | 2.449 | Cond. No. | 58.5 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 06:26:38 | 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: | 0.003682 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.952 |
| Time: | 06:26:38 | 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 | 84.2058 | 74.322 | 1.133 | 0.270 | -70.356 238.767 |
| expression | -0.8516 | 14.036 | -0.061 | 0.952 | -30.040 28.337 |
| Omnibus: | 3.310 | Durbin-Watson: | 2.472 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.571 |
| Skew: | 0.289 | Prob(JB): | 0.456 |
| Kurtosis: | 1.858 | Cond. No. | 56.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.575 | 0.135 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.603 |
| Model: | OLS | Adj. R-squared: | 0.495 |
| Method: | Least Squares | F-statistic: | 5.571 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0143 |
| Time: | 06:26:38 | Log-Likelihood: | -68.370 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 11 | BIC: | 147.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.6975 | 138.505 | 0.532 | 0.605 | -231.150 378.544 |
| C(dose)[T.1] | -147.8204 | 166.691 | -0.887 | 0.394 | -514.705 219.064 |
| expression | -0.9451 | 20.825 | -0.045 | 0.965 | -46.780 44.890 |
| expression:C(dose)[T.1] | 32.3916 | 25.788 | 1.256 | 0.235 | -24.366 89.150 |
| Omnibus: | 2.399 | Durbin-Watson: | 0.809 |
| Prob(Omnibus): | 0.301 | Jarque-Bera (JB): | 1.163 |
| Skew: | -0.681 | Prob(JB): | 0.559 |
| Kurtosis: | 3.076 | Cond. No. | 222. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.546 |
| Model: | OLS | Adj. R-squared: | 0.471 |
| Method: | Least Squares | F-statistic: | 7.221 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00874 |
| Time: | 06:26:38 | Log-Likelihood: | -69.375 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 12 | BIC: | 146.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -66.4151 | 84.057 | -0.790 | 0.445 | -249.560 116.730 |
| C(dose)[T.1] | 60.6415 | 15.964 | 3.799 | 0.003 | 25.860 95.423 |
| expression | 20.1785 | 12.575 | 1.605 | 0.135 | -7.219 47.576 |
| Omnibus: | 0.888 | Durbin-Watson: | 0.684 |
| Prob(Omnibus): | 0.642 | Jarque-Bera (JB): | 0.752 |
| Skew: | -0.470 | Prob(JB): | 0.687 |
| Kurtosis: | 2.437 | Cond. No. | 77.4 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 06:26:38 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.005259 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.943 |
| Time: | 06:26:38 | Log-Likelihood: | -75.297 |
| 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 | 101.0308 | 102.053 | 0.990 | 0.340 | -119.441 321.503 |
| expression | -1.1633 | 16.041 | -0.073 | 0.943 | -35.817 33.491 |
| Omnibus: | 0.539 | Durbin-Watson: | 1.616 |
| Prob(Omnibus): | 0.764 | Jarque-Bera (JB): | 0.558 |
| Skew: | 0.053 | Prob(JB): | 0.757 |
| Kurtosis: | 2.062 | Cond. No. | 65.5 |