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 |
| 0.022 | 0.884 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.80 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000136 |
| Time: | 04:49:06 | Log-Likelihood: | -101.01 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.7711 | 189.055 | 0.290 | 0.775 | -340.926 450.468 |
| C(dose)[T.1] | -48.5718 | 382.373 | -0.127 | 0.900 | -848.887 751.744 |
| expression | -0.0636 | 21.345 | -0.003 | 0.998 | -44.738 44.611 |
| expression:C(dose)[T.1] | 11.9560 | 44.454 | 0.269 | 0.791 | -81.086 104.998 |
| Omnibus: | 0.459 | Durbin-Watson: | 1.885 |
| Prob(Omnibus): | 0.795 | Jarque-Bera (JB): | 0.576 |
| Skew: | 0.151 | Prob(JB): | 0.750 |
| Kurtosis: | 2.286 | Cond. No. | 875. |
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: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 04:49:06 | 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 | 30.3696 | 161.971 | 0.188 | 0.853 | -307.495 368.234 |
| C(dose)[T.1] | 54.2273 | 10.647 | 5.093 | 0.000 | 32.018 76.437 |
| expression | 2.6929 | 18.284 | 0.147 | 0.884 | -35.446 40.832 |
| Omnibus: | 0.389 | Durbin-Watson: | 1.859 |
| Prob(Omnibus): | 0.823 | Jarque-Bera (JB): | 0.522 |
| Skew: | 0.046 | Prob(JB): | 0.770 |
| Kurtosis: | 2.268 | Cond. No. | 327. |
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: | 04:49:06 | 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: | 0.195 |
| Model: | OLS | Adj. R-squared: | 0.156 |
| Method: | Least Squares | F-statistic: | 5.079 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0350 |
| Time: | 04:49:06 | Log-Likelihood: | -110.61 |
| No. Observations: | 23 | AIC: | 225.2 |
| Df Residuals: | 21 | BIC: | 227.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 515.9309 | 193.671 | 2.664 | 0.015 | 113.169 918.692 |
| expression | -50.1719 | 22.263 | -2.254 | 0.035 | -96.470 -3.873 |
| Omnibus: | 3.017 | Durbin-Watson: | 2.215 |
| Prob(Omnibus): | 0.221 | Jarque-Bera (JB): | 1.321 |
| Skew: | 0.118 | Prob(JB): | 0.517 |
| Kurtosis: | 1.850 | Cond. No. | 264. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.688 | 0.218 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.517 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 3.921 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0397 |
| Time: | 04:49:06 | Log-Likelihood: | -69.845 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 410.5492 | 319.470 | 1.285 | 0.225 | -292.601 1113.699 |
| C(dose)[T.1] | 28.4238 | 605.406 | 0.047 | 0.963 | -1304.066 1360.914 |
| expression | -35.4948 | 33.028 | -1.075 | 0.306 | -108.189 37.199 |
| expression:C(dose)[T.1] | 1.9243 | 62.912 | 0.031 | 0.976 | -136.544 140.392 |
| Omnibus: | 2.028 | Durbin-Watson: | 0.852 |
| Prob(Omnibus): | 0.363 | Jarque-Bera (JB): | 1.414 |
| Skew: | -0.722 | Prob(JB): | 0.493 |
| Kurtosis: | 2.579 | Cond. No. | 932. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.517 |
| Model: | OLS | Adj. R-squared: | 0.436 |
| Method: | Least Squares | F-statistic: | 6.416 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0127 |
| Time: | 04:49:06 | Log-Likelihood: | -69.846 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 405.4223 | 260.401 | 1.557 | 0.145 | -161.944 972.788 |
| C(dose)[T.1] | 46.9354 | 14.840 | 3.163 | 0.008 | 14.602 79.269 |
| expression | -34.9645 | 26.915 | -1.299 | 0.218 | -93.607 23.678 |
| Omnibus: | 1.990 | Durbin-Watson: | 0.847 |
| Prob(Omnibus): | 0.370 | Jarque-Bera (JB): | 1.392 |
| Skew: | -0.715 | Prob(JB): | 0.499 |
| Kurtosis: | 2.574 | Cond. No. | 345. |
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: | 04:49:06 | 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: | 0.114 |
| Model: | OLS | Adj. R-squared: | 0.046 |
| Method: | Least Squares | F-statistic: | 1.671 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.219 |
| Time: | 04:49:06 | Log-Likelihood: | -74.393 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 526.6212 | 335.090 | 1.572 | 0.140 | -197.297 1250.539 |
| expression | -44.9483 | 34.774 | -1.293 | 0.219 | -120.073 30.176 |
| Omnibus: | 1.274 | Durbin-Watson: | 1.777 |
| Prob(Omnibus): | 0.529 | Jarque-Bera (JB): | 0.900 |
| Skew: | 0.294 | Prob(JB): | 0.638 |
| Kurtosis: | 1.954 | Cond. No. | 341. |