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 |
| 0.445 | 0.512 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.653 |
| Method: | Least Squares | F-statistic: | 14.79 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.31e-05 |
| Time: | 23:00:00 | Log-Likelihood: | -99.252 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 19 | BIC: | 211.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.2931 | 25.328 | 0.604 | 0.553 | -37.718 68.304 |
| C(dose)[T.1] | 121.1415 | 42.311 | 2.863 | 0.010 | 32.584 209.699 |
| expression | 9.3515 | 5.927 | 1.578 | 0.131 | -3.055 21.758 |
| expression:C(dose)[T.1] | -15.8094 | 9.525 | -1.660 | 0.113 | -35.746 4.127 |
| Omnibus: | 0.413 | Durbin-Watson: | 1.465 |
| Prob(Omnibus): | 0.814 | Jarque-Bera (JB): | 0.472 |
| Skew: | -0.272 | Prob(JB): | 0.790 |
| Kurtosis: | 2.556 | Cond. No. | 58.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.13 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.27e-05 |
| Time: | 23:00:00 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 40.7682 | 21.012 | 1.940 | 0.067 | -3.062 84.599 |
| C(dose)[T.1] | 52.3292 | 8.804 | 5.944 | 0.000 | 33.964 70.695 |
| expression | 3.2297 | 4.839 | 0.667 | 0.512 | -6.865 13.324 |
| Omnibus: | 0.225 | Durbin-Watson: | 1.800 |
| Prob(Omnibus): | 0.894 | Jarque-Bera (JB): | 0.422 |
| Skew: | 0.098 | Prob(JB): | 0.810 |
| Kurtosis: | 2.366 | Cond. No. | 22.5 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:00:00 | 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: | 0.050 |
| Model: | OLS | Adj. R-squared: | 0.005 |
| Method: | Least Squares | F-statistic: | 1.113 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.303 |
| Time: | 23:00:00 | Log-Likelihood: | -112.51 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.5288 | 34.090 | 1.306 | 0.206 | -26.366 115.423 |
| expression | 8.1632 | 7.738 | 1.055 | 0.303 | -7.929 24.256 |
| Omnibus: | 1.907 | Durbin-Watson: | 2.364 |
| Prob(Omnibus): | 0.385 | Jarque-Bera (JB): | 1.636 |
| Skew: | 0.547 | Prob(JB): | 0.441 |
| Kurtosis: | 2.285 | Cond. No. | 22.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.140 | 0.715 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.481 |
| Model: | OLS | Adj. R-squared: | 0.340 |
| Method: | Least Squares | F-statistic: | 3.403 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0570 |
| Time: | 23:00:00 | Log-Likelihood: | -70.376 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 40.1340 | 69.719 | 0.576 | 0.576 | -113.317 193.585 |
| C(dose)[T.1] | 112.4342 | 86.042 | 1.307 | 0.218 | -76.944 301.812 |
| expression | 4.5000 | 11.333 | 0.397 | 0.699 | -20.444 29.444 |
| expression:C(dose)[T.1] | -10.3418 | 13.873 | -0.745 | 0.472 | -40.877 20.193 |
| Omnibus: | 4.084 | Durbin-Watson: | 0.733 |
| Prob(Omnibus): | 0.130 | Jarque-Bera (JB): | 2.395 |
| Skew: | -0.978 | Prob(JB): | 0.302 |
| Kurtosis: | 3.076 | Cond. No. | 99.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.012 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0262 |
| Time: | 23:00:01 | Log-Likelihood: | -70.746 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.9941 | 40.550 | 2.022 | 0.066 | -6.357 170.346 |
| C(dose)[T.1] | 49.4060 | 15.659 | 3.155 | 0.008 | 15.289 83.523 |
| expression | -2.4014 | 6.414 | -0.374 | 0.715 | -16.377 11.575 |
| Omnibus: | 3.941 | Durbin-Watson: | 0.782 |
| Prob(Omnibus): | 0.139 | Jarque-Bera (JB): | 2.533 |
| Skew: | -1.004 | Prob(JB): | 0.282 |
| Kurtosis: | 2.867 | Cond. No. | 33.3 |
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: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:00:01 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.04056 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.844 |
| Time: | 23:00:01 | Log-Likelihood: | -75.277 |
| 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 | 103.9210 | 51.918 | 2.002 | 0.067 | -8.242 216.084 |
| expression | -1.6777 | 8.331 | -0.201 | 0.844 | -19.675 16.320 |
| Omnibus: | 0.473 | Durbin-Watson: | 1.660 |
| Prob(Omnibus): | 0.789 | Jarque-Bera (JB): | 0.530 |
| Skew: | -0.034 | Prob(JB): | 0.767 |
| Kurtosis: | 2.082 | Cond. No. | 32.7 |