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 |
| 0.017 | 0.898 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.97 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.000125 |
| Time: | 22:28:03 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -41.8752 | 240.138 | -0.174 | 0.863 | -544.490 460.739 |
| C(dose)[T.1] | 261.9359 | 415.149 | 0.631 | 0.536 | -606.981 1130.852 |
| expression | 9.3339 | 23.320 | 0.400 | 0.693 | -39.476 58.144 |
| expression:C(dose)[T.1] | -19.9497 | 39.552 | -0.504 | 0.620 | -102.733 62.833 |
| Omnibus: | 0.484 | Durbin-Watson: | 1.915 |
| Prob(Omnibus): | 0.785 | Jarque-Bera (JB): | 0.581 |
| Skew: | 0.114 | Prob(JB): | 0.748 |
| Kurtosis: | 2.255 | Cond. No. | 1.19e+03 |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 2.81e-05 |
| Time: | 22:28:03 | 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 | 29.5172 | 190.340 | 0.155 | 0.878 | -367.526 426.560 |
| C(dose)[T.1] | 52.6059 | 10.420 | 5.048 | 0.000 | 30.869 74.342 |
| expression | 2.3986 | 18.481 | 0.130 | 0.898 | -36.152 40.949 |
| Omnibus: | 0.334 | Durbin-Watson: | 1.916 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.493 |
| Skew: | 0.063 | Prob(JB): | 0.782 |
| Kurtosis: | 2.294 | Cond. No. | 459. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:28:03 | 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: | 0.203 |
| Model: | OLS | Adj. R-squared: | 0.165 |
| Method: | Least Squares | F-statistic: | 5.333 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0312 |
| Time: | 22:28:03 | Log-Likelihood: | -110.50 |
| No. Observations: | 23 | AIC: | 225.0 |
| Df Residuals: | 21 | BIC: | 227.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -471.9225 | 238.964 | -1.975 | 0.062 | -968.876 25.031 |
| expression | 52.8401 | 22.881 | 2.309 | 0.031 | 5.256 100.425 |
| Omnibus: | 3.201 | Durbin-Watson: | 2.564 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.560 |
| Skew: | 0.296 | Prob(JB): | 0.458 |
| Kurtosis: | 1.870 | Cond. No. | 391. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.340 | 0.571 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.318 |
| Method: | Least Squares | F-statistic: | 3.174 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0675 |
| Time: | 22:28:03 | Log-Likelihood: | -70.623 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -40.7538 | 475.280 | -0.086 | 0.933 | -1086.838 1005.331 |
| C(dose)[T.1] | 45.8513 | 523.311 | 0.088 | 0.932 | -1105.948 1197.650 |
| expression | 10.2854 | 45.173 | 0.228 | 0.824 | -89.140 109.710 |
| expression:C(dose)[T.1] | 0.3652 | 49.768 | 0.007 | 0.994 | -109.174 109.904 |
| Omnibus: | 1.330 | Durbin-Watson: | 0.873 |
| Prob(Omnibus): | 0.514 | Jarque-Bera (JB): | 0.900 |
| Skew: | -0.570 | Prob(JB): | 0.638 |
| Kurtosis: | 2.624 | Cond. No. | 1.05e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.375 |
| Method: | Least Squares | F-statistic: | 5.193 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.0237 |
| Time: | 22:28:03 | Log-Likelihood: | -70.623 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -43.9182 | 191.258 | -0.230 | 0.822 | -460.634 372.798 |
| C(dose)[T.1] | 49.6893 | 15.544 | 3.197 | 0.008 | 15.821 83.557 |
| expression | 10.5862 | 18.152 | 0.583 | 0.571 | -28.963 50.136 |
| Omnibus: | 1.333 | Durbin-Watson: | 0.870 |
| Prob(Omnibus): | 0.513 | Jarque-Bera (JB): | 0.904 |
| Skew: | -0.571 | Prob(JB): | 0.636 |
| Kurtosis: | 2.622 | Cond. No. | 262. |
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: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:28:03 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.069 |
| Method: | Least Squares | F-statistic: | 0.09837 |
| Date: | Mon, 27 Jan 2025 | Prob (F-statistic): | 0.759 |
| Time: | 22:28:03 | Log-Likelihood: | -75.244 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.6845 | 248.847 | 0.063 | 0.951 | -521.917 553.286 |
| expression | 7.4316 | 23.695 | 0.314 | 0.759 | -43.759 58.622 |
| Omnibus: | 0.295 | Durbin-Watson: | 1.627 |
| Prob(Omnibus): | 0.863 | Jarque-Bera (JB): | 0.452 |
| Skew: | 0.113 | Prob(JB): | 0.798 |
| Kurtosis: | 2.180 | Cond. No. | 260. |