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 |
| 0.517 | 0.481 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.627 |
| Method: | Least Squares | F-statistic: | 13.31 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 6.50e-05 |
| Time: | 19:26:48 | Log-Likelihood: | -100.09 |
| No. Observations: | 23 | AIC: | 208.2 |
| Df Residuals: | 19 | BIC: | 212.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -94.4388 | 120.788 | -0.782 | 0.444 | -347.250 158.373 |
| C(dose)[T.1] | 268.8691 | 205.608 | 1.308 | 0.207 | -161.474 699.212 |
| expression | 19.5740 | 15.886 | 1.232 | 0.233 | -13.676 52.824 |
| expression:C(dose)[T.1] | -27.7265 | 25.751 | -1.077 | 0.295 | -81.624 26.171 |
| Omnibus: | 0.205 | Durbin-Watson: | 2.049 |
| Prob(Omnibus): | 0.902 | Jarque-Bera (JB): | 0.410 |
| Skew: | -0.024 | Prob(JB): | 0.815 |
| Kurtosis: | 2.348 | Cond. No. | 473. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.23 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.20e-05 |
| Time: | 19:26:48 | Log-Likelihood: | -100.77 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.3037 | 95.513 | -0.150 | 0.882 | -213.540 184.933 |
| C(dose)[T.1] | 47.8329 | 11.560 | 4.138 | 0.001 | 23.720 71.946 |
| expression | 9.0217 | 12.553 | 0.719 | 0.481 | -17.162 35.206 |
| Omnibus: | 0.210 | Durbin-Watson: | 1.956 |
| Prob(Omnibus): | 0.900 | Jarque-Bera (JB): | 0.413 |
| Skew: | -0.013 | Prob(JB): | 0.814 |
| Kurtosis: | 2.344 | Cond. No. | 178. |
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, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 19:26:48 | 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: | 0.365 |
| Model: | OLS | Adj. R-squared: | 0.335 |
| Method: | Least Squares | F-statistic: | 12.07 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00227 |
| Time: | 19:26:48 | Log-Likelihood: | -107.88 |
| No. Observations: | 23 | AIC: | 219.8 |
| Df Residuals: | 21 | BIC: | 222.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -262.7950 | 98.750 | -2.661 | 0.015 | -468.158 -57.432 |
| expression | 43.4336 | 12.501 | 3.474 | 0.002 | 17.436 69.431 |
| Omnibus: | 2.449 | Durbin-Watson: | 2.086 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.612 |
| Skew: | 0.648 | Prob(JB): | 0.447 |
| Kurtosis: | 2.973 | Cond. No. | 138. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.433 | 0.523 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.548 |
| Model: | OLS | Adj. R-squared: | 0.424 |
| Method: | Least Squares | F-statistic: | 4.438 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0282 |
| Time: | 19:26:48 | Log-Likelihood: | -69.351 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 11 | BIC: | 149.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.6210 | 75.692 | 1.197 | 0.256 | -75.975 257.217 |
| C(dose)[T.1] | -113.0852 | 119.249 | -0.948 | 0.363 | -375.550 149.380 |
| expression | -4.0420 | 13.055 | -0.310 | 0.763 | -32.775 24.691 |
| expression:C(dose)[T.1] | 29.7047 | 21.353 | 1.391 | 0.192 | -17.293 76.703 |
| Omnibus: | 0.851 | Durbin-Watson: | 1.198 |
| Prob(Omnibus): | 0.654 | Jarque-Bera (JB): | 0.420 |
| Skew: | -0.398 | Prob(JB): | 0.811 |
| Kurtosis: | 2.803 | Cond. No. | 117. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.278 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0227 |
| Time: | 19:26:48 | Log-Likelihood: | -70.567 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.9140 | 62.570 | 0.430 | 0.675 | -109.415 163.243 |
| C(dose)[T.1] | 51.4412 | 15.834 | 3.249 | 0.007 | 16.941 85.941 |
| expression | 7.0610 | 10.726 | 0.658 | 0.523 | -16.309 30.431 |
| Omnibus: | 1.756 | Durbin-Watson: | 0.773 |
| Prob(Omnibus): | 0.416 | Jarque-Bera (JB): | 1.370 |
| Skew: | -0.666 | Prob(JB): | 0.504 |
| Kurtosis: | 2.354 | Cond. No. | 47.3 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 19:26:48 | 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: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.001030 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.975 |
| Time: | 19:26:48 | Log-Likelihood: | -75.299 |
| 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 | 96.1319 | 77.490 | 1.241 | 0.237 | -71.275 263.539 |
| expression | -0.4427 | 13.796 | -0.032 | 0.975 | -30.248 29.362 |
| Omnibus: | 0.586 | Durbin-Watson: | 1.619 |
| Prob(Omnibus): | 0.746 | Jarque-Bera (JB): | 0.575 |
| Skew: | 0.047 | Prob(JB): | 0.750 |
| Kurtosis: | 2.046 | Cond. No. | 44.2 |