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 |
| 0.023 | 0.881 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.09 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000118 |
| Time: | 05:19:56 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.1428 | 131.068 | 0.085 | 0.933 | -263.186 285.471 |
| C(dose)[T.1] | 170.3520 | 190.752 | 0.893 | 0.383 | -228.896 569.600 |
| expression | 5.6098 | 17.054 | 0.329 | 0.746 | -30.085 41.305 |
| expression:C(dose)[T.1] | -14.5988 | 23.959 | -0.609 | 0.550 | -64.746 35.548 |
| Omnibus: | 0.714 | Durbin-Watson: | 1.971 |
| Prob(Omnibus): | 0.700 | Jarque-Bera (JB): | 0.723 |
| Skew: | 0.196 | Prob(JB): | 0.697 |
| Kurtosis: | 2.225 | Cond. No. | 450. |
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: | 05:19:56 | 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 | 67.9266 | 90.703 | 0.749 | 0.463 | -121.276 257.129 |
| C(dose)[T.1] | 54.3198 | 10.902 | 4.983 | 0.000 | 31.579 77.061 |
| expression | -1.7870 | 11.789 | -0.152 | 0.881 | -26.378 22.804 |
| Omnibus: | 0.469 | Durbin-Watson: | 1.897 |
| Prob(Omnibus): | 0.791 | Jarque-Bera (JB): | 0.563 |
| Skew: | 0.047 | Prob(JB): | 0.755 |
| Kurtosis: | 2.239 | Cond. No. | 168. |
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: | 05:19:56 | 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: | 0.214 |
| Model: | OLS | Adj. R-squared: | 0.177 |
| Method: | Least Squares | F-statistic: | 5.728 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0261 |
| Time: | 05:19:56 | Log-Likelihood: | -110.33 |
| No. Observations: | 23 | AIC: | 224.7 |
| Df Residuals: | 21 | BIC: | 226.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -183.4298 | 110.132 | -1.666 | 0.111 | -412.462 45.602 |
| expression | 33.1428 | 13.847 | 2.393 | 0.026 | 4.345 61.940 |
| Omnibus: | 1.801 | Durbin-Watson: | 2.028 |
| Prob(Omnibus): | 0.406 | Jarque-Bera (JB): | 1.565 |
| Skew: | 0.575 | Prob(JB): | 0.457 |
| Kurtosis: | 2.445 | Cond. No. | 139. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.422 | 0.528 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.575 |
| Model: | OLS | Adj. R-squared: | 0.460 |
| Method: | Least Squares | F-statistic: | 4.969 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0203 |
| Time: | 05:19:56 | Log-Likelihood: | -68.875 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 11 | BIC: | 148.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -22.9755 | 99.270 | -0.231 | 0.821 | -241.468 195.517 |
| C(dose)[T.1] | 249.3792 | 122.013 | 2.044 | 0.066 | -19.169 517.927 |
| expression | 14.0808 | 15.374 | 0.916 | 0.379 | -19.758 47.920 |
| expression:C(dose)[T.1] | -32.1917 | 19.254 | -1.672 | 0.123 | -74.569 10.185 |
| Omnibus: | 0.454 | Durbin-Watson: | 0.997 |
| Prob(Omnibus): | 0.797 | Jarque-Bera (JB): | 0.172 |
| Skew: | -0.243 | Prob(JB): | 0.918 |
| Kurtosis: | 2.804 | Cond. No. | 154. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.268 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0228 |
| Time: | 05:19:56 | Log-Likelihood: | -70.574 |
| 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 | 108.8144 | 64.704 | 1.682 | 0.118 | -32.163 249.792 |
| C(dose)[T.1] | 46.8831 | 15.875 | 2.953 | 0.012 | 12.295 81.471 |
| expression | -6.4460 | 9.923 | -0.650 | 0.528 | -28.067 15.175 |
| Omnibus: | 1.843 | Durbin-Watson: | 0.992 |
| Prob(Omnibus): | 0.398 | Jarque-Bera (JB): | 1.300 |
| Skew: | -0.687 | Prob(JB): | 0.522 |
| Kurtosis: | 2.562 | Cond. No. | 54.3 |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 05:19:56 | 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: | 0.080 |
| Model: | OLS | Adj. R-squared: | 0.010 |
| Method: | Least Squares | F-statistic: | 1.137 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.306 |
| Time: | 05:19:56 | Log-Likelihood: | -74.671 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 174.7698 | 76.671 | 2.279 | 0.040 | 9.132 340.408 |
| expression | -13.0203 | 12.209 | -1.066 | 0.306 | -39.396 13.356 |
| Omnibus: | 0.192 | Durbin-Watson: | 1.539 |
| Prob(Omnibus): | 0.909 | Jarque-Bera (JB): | 0.139 |
| Skew: | -0.166 | Prob(JB): | 0.933 |
| Kurtosis: | 2.665 | Cond. No. | 50.6 |