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 |
| 0.015 | 0.905 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.91 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.000129 |
| Time: | 01:08:59 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.4784 | 39.724 | 0.969 | 0.345 | -44.665 121.622 |
| C(dose)[T.1] | 76.3845 | 53.847 | 1.419 | 0.172 | -36.319 189.088 |
| expression | 3.5183 | 8.777 | 0.401 | 0.693 | -14.851 21.888 |
| expression:C(dose)[T.1] | -5.1534 | 11.871 | -0.434 | 0.669 | -30.000 19.693 |
| Omnibus: | 0.356 | Durbin-Watson: | 1.880 |
| Prob(Omnibus): | 0.837 | Jarque-Bera (JB): | 0.512 |
| Skew: | 0.136 | Prob(JB): | 0.774 |
| Kurtosis: | 2.322 | Cond. No. | 75.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.52 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 2.81e-05 |
| Time: | 01:08:59 | Log-Likelihood: | -101.05 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.0723 | 26.580 | 1.921 | 0.069 | -4.374 106.518 |
| C(dose)[T.1] | 53.3339 | 8.767 | 6.084 | 0.000 | 35.047 71.621 |
| expression | 0.7014 | 5.789 | 0.121 | 0.905 | -11.373 12.776 |
| Omnibus: | 0.280 | Durbin-Watson: | 1.866 |
| Prob(Omnibus): | 0.869 | Jarque-Bera (JB): | 0.459 |
| Skew: | 0.058 | Prob(JB): | 0.795 |
| Kurtosis: | 2.317 | Cond. No. | 29.0 |
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: | Wed, 29 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 01:08:59 | 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: | 0.007156 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.933 |
| Time: | 01:08:59 | 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 | 76.1086 | 43.268 | 1.759 | 0.093 | -13.872 166.089 |
| expression | 0.8068 | 9.538 | 0.085 | 0.933 | -19.028 20.641 |
| Omnibus: | 3.479 | Durbin-Watson: | 2.467 |
| Prob(Omnibus): | 0.176 | Jarque-Bera (JB): | 1.612 |
| Skew: | 0.294 | Prob(JB): | 0.447 |
| Kurtosis: | 1.844 | Cond. No. | 28.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.972 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.483 |
| Model: | OLS | Adj. R-squared: | 0.343 |
| Method: | Least Squares | F-statistic: | 3.431 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.0559 |
| Time: | 01:08:59 | Log-Likelihood: | -70.346 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.6355 | 62.899 | 1.791 | 0.101 | -25.805 251.076 |
| C(dose)[T.1] | -16.4929 | 78.348 | -0.211 | 0.837 | -188.935 155.949 |
| expression | -9.5464 | 13.054 | -0.731 | 0.480 | -38.278 19.185 |
| expression:C(dose)[T.1] | 13.5158 | 15.745 | 0.858 | 0.409 | -21.139 48.171 |
| Omnibus: | 1.076 | Durbin-Watson: | 0.940 |
| Prob(Omnibus): | 0.584 | Jarque-Bera (JB): | 0.908 |
| Skew: | -0.514 | Prob(JB): | 0.635 |
| Kurtosis: | 2.371 | Cond. No. | 75.4 |
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: | 4.886 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 01:08:59 | Log-Likelihood: | -70.832 |
| 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 | 68.6426 | 36.064 | 1.903 | 0.081 | -9.935 147.220 |
| C(dose)[T.1] | 49.3053 | 16.034 | 3.075 | 0.010 | 14.369 84.241 |
| expression | -0.2564 | 7.219 | -0.036 | 0.972 | -15.984 15.472 |
| Omnibus: | 2.729 | Durbin-Watson: | 0.803 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.869 |
| Skew: | -0.845 | Prob(JB): | 0.393 |
| Kurtosis: | 2.631 | Cond. No. | 24.3 |
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: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 01:08:59 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.061 |
| Method: | Least Squares | F-statistic: | 0.1918 |
| Date: | Wed, 29 Jan 2025 | Prob (F-statistic): | 0.669 |
| Time: | 01:08:59 | Log-Likelihood: | -75.190 |
| 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 | 73.8854 | 46.280 | 1.597 | 0.134 | -26.095 173.866 |
| expression | 3.9866 | 9.103 | 0.438 | 0.669 | -15.679 23.652 |
| Omnibus: | 0.042 | Durbin-Watson: | 1.635 |
| Prob(Omnibus): | 0.979 | Jarque-Bera (JB): | 0.265 |
| Skew: | -0.049 | Prob(JB): | 0.876 |
| Kurtosis: | 2.357 | Cond. No. | 24.2 |