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 |
| 0.505 | 0.486 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.72 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.64e-05 |
| Time: | 04:48:12 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 42.7495 | 140.750 | 0.304 | 0.765 | -251.843 337.342 |
| C(dose)[T.1] | -108.3173 | 220.024 | -0.492 | 0.628 | -568.832 352.198 |
| expression | 1.5286 | 18.759 | 0.081 | 0.936 | -37.734 40.792 |
| expression:C(dose)[T.1] | 22.5454 | 30.070 | 0.750 | 0.463 | -40.392 85.483 |
| Omnibus: | 0.130 | Durbin-Watson: | 1.949 |
| Prob(Omnibus): | 0.937 | Jarque-Bera (JB): | 0.343 |
| Skew: | -0.087 | Prob(JB): | 0.842 |
| Kurtosis: | 2.428 | Cond. No. | 465. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.21e-05 |
| Time: | 04:48:12 | Log-Likelihood: | -100.78 |
| 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 | -23.0219 | 108.857 | -0.211 | 0.835 | -250.093 204.050 |
| C(dose)[T.1] | 56.4820 | 9.727 | 5.807 | 0.000 | 36.193 76.771 |
| expression | 10.3027 | 14.500 | 0.711 | 0.486 | -19.943 40.549 |
| Omnibus: | 0.346 | Durbin-Watson: | 1.874 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.498 |
| Skew: | 0.034 | Prob(JB): | 0.780 |
| Kurtosis: | 2.283 | Cond. No. | 189. |
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: | 04:48:12 | 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: | 0.081 |
| Model: | OLS | Adj. R-squared: | 0.037 |
| Method: | Least Squares | F-statistic: | 1.840 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.189 |
| Time: | 04:48:12 | Log-Likelihood: | -112.14 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 285.6083 | 151.946 | 1.880 | 0.074 | -30.380 601.597 |
| expression | -28.0119 | 20.651 | -1.356 | 0.189 | -70.958 14.934 |
| Omnibus: | 0.603 | Durbin-Watson: | 2.500 |
| Prob(Omnibus): | 0.740 | Jarque-Bera (JB): | 0.670 |
| Skew: | 0.310 | Prob(JB): | 0.715 |
| Kurtosis: | 2.438 | Cond. No. | 165. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.059 | 0.812 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.226 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0649 |
| Time: | 04:48:12 | Log-Likelihood: | -70.566 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.4930 | 192.372 | 0.507 | 0.622 | -325.916 520.902 |
| C(dose)[T.1] | -137.5154 | 320.519 | -0.429 | 0.676 | -842.973 567.942 |
| expression | -3.7041 | 23.657 | -0.157 | 0.878 | -55.772 48.364 |
| expression:C(dose)[T.1] | 23.3706 | 39.921 | 0.585 | 0.570 | -64.495 111.236 |
| Omnibus: | 4.678 | Durbin-Watson: | 0.961 |
| Prob(Omnibus): | 0.096 | Jarque-Bera (JB): | 2.607 |
| Skew: | -1.011 | Prob(JB): | 0.272 |
| Kurtosis: | 3.287 | Cond. No. | 405. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.938 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
| Time: | 04:48:12 | Log-Likelihood: | -70.796 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.8812 | 150.806 | 0.205 | 0.841 | -297.697 359.459 |
| C(dose)[T.1] | 49.8777 | 15.949 | 3.127 | 0.009 | 15.127 84.628 |
| expression | 4.5029 | 18.526 | 0.243 | 0.812 | -35.863 44.868 |
| Omnibus: | 3.025 | Durbin-Watson: | 0.800 |
| Prob(Omnibus): | 0.220 | Jarque-Bera (JB): | 1.992 |
| Skew: | -0.882 | Prob(JB): | 0.369 |
| Kurtosis: | 2.726 | Cond. No. | 158. |
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: | 04:48:12 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.05789 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.814 |
| Time: | 04:48:13 | Log-Likelihood: | -75.267 |
| 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 | 139.3068 | 189.969 | 0.733 | 0.476 | -271.096 549.709 |
| expression | -5.6796 | 23.607 | -0.241 | 0.814 | -56.679 45.319 |
| Omnibus: | 0.646 | Durbin-Watson: | 1.610 |
| Prob(Omnibus): | 0.724 | Jarque-Bera (JB): | 0.602 |
| Skew: | 0.086 | Prob(JB): | 0.740 |
| Kurtosis: | 2.034 | Cond. No. | 153. |