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 |
| 4.719 | 0.042 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.717 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 16.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.90e-05 |
| Time: | 03:42:18 | Log-Likelihood: | -98.570 |
| No. Observations: | 23 | AIC: | 205.1 |
| Df Residuals: | 19 | BIC: | 209.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.2032 | 121.047 | 1.935 | 0.068 | -19.151 487.557 |
| C(dose)[T.1] | 102.3240 | 191.531 | 0.534 | 0.599 | -298.556 503.204 |
| expression | -20.1480 | 13.535 | -1.489 | 0.153 | -48.477 8.181 |
| expression:C(dose)[T.1] | -6.7640 | 22.069 | -0.306 | 0.763 | -52.956 39.428 |
| Omnibus: | 1.513 | Durbin-Watson: | 1.939 |
| Prob(Omnibus): | 0.469 | Jarque-Bera (JB): | 1.335 |
| Skew: | 0.468 | Prob(JB): | 0.513 |
| Kurtosis: | 2.282 | Cond. No. | 516. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.688 |
| Method: | Least Squares | F-statistic: | 25.22 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.41e-06 |
| Time: | 03:42:18 | Log-Likelihood: | -98.626 |
| No. Observations: | 23 | AIC: | 203.3 |
| Df Residuals: | 20 | BIC: | 206.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 256.9320 | 93.478 | 2.749 | 0.012 | 61.940 451.924 |
| C(dose)[T.1] | 43.6906 | 9.052 | 4.826 | 0.000 | 24.808 62.573 |
| expression | -22.6922 | 10.446 | -2.172 | 0.042 | -44.482 -0.903 |
| Omnibus: | 1.392 | Durbin-Watson: | 1.946 |
| Prob(Omnibus): | 0.499 | Jarque-Bera (JB): | 1.199 |
| Skew: | 0.398 | Prob(JB): | 0.549 |
| Kurtosis: | 2.214 | Cond. No. | 211. |
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: | 03:42:18 | 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: | 0.385 |
| Model: | OLS | Adj. R-squared: | 0.356 |
| Method: | Least Squares | F-statistic: | 13.17 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00157 |
| Time: | 03:42:18 | Log-Likelihood: | -107.51 |
| No. Observations: | 23 | AIC: | 219.0 |
| Df Residuals: | 21 | BIC: | 221.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 493.7364 | 114.245 | 4.322 | 0.000 | 256.151 731.322 |
| expression | -47.4231 | 13.070 | -3.628 | 0.002 | -74.604 -20.243 |
| Omnibus: | 0.611 | Durbin-Watson: | 2.064 |
| Prob(Omnibus): | 0.737 | Jarque-Bera (JB): | 0.302 |
| Skew: | 0.276 | Prob(JB): | 0.860 |
| Kurtosis: | 2.896 | Cond. No. | 179. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.466 | 0.249 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.636 |
| Model: | OLS | Adj. R-squared: | 0.536 |
| Method: | Least Squares | F-statistic: | 6.394 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00910 |
| Time: | 03:42:18 | Log-Likelihood: | -67.730 |
| No. Observations: | 15 | AIC: | 143.5 |
| Df Residuals: | 11 | BIC: | 146.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 148.8741 | 110.758 | 1.344 | 0.206 | -94.904 392.652 |
| C(dose)[T.1] | 849.6193 | 406.289 | 2.091 | 0.061 | -44.616 1743.854 |
| expression | -8.9108 | 12.071 | -0.738 | 0.476 | -35.478 17.657 |
| expression:C(dose)[T.1] | -83.5211 | 42.702 | -1.956 | 0.076 | -177.508 10.466 |
| Omnibus: | 0.160 | Durbin-Watson: | 1.085 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.354 |
| Skew: | 0.151 | Prob(JB): | 0.838 |
| Kurtosis: | 2.310 | Cond. No. | 679. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 6.215 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0140 |
| Time: | 03:42:18 | Log-Likelihood: | -69.968 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 209.8721 | 118.128 | 1.777 | 0.101 | -47.508 467.252 |
| C(dose)[T.1] | 55.4409 | 15.727 | 3.525 | 0.004 | 21.174 89.708 |
| expression | -15.5846 | 12.870 | -1.211 | 0.249 | -43.625 12.456 |
| Omnibus: | 1.032 | Durbin-Watson: | 1.070 |
| Prob(Omnibus): | 0.597 | Jarque-Bera (JB): | 0.872 |
| Skew: | -0.505 | Prob(JB): | 0.647 |
| Kurtosis: | 2.388 | Cond. No. | 151. |
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: | 03:42:18 | 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: | 0.001813 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.967 |
| Time: | 03:42:18 | 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 | 100.3045 | 156.219 | 0.642 | 0.532 | -237.186 437.795 |
| expression | -0.7096 | 16.666 | -0.043 | 0.967 | -36.714 35.295 |
| Omnibus: | 0.647 | Durbin-Watson: | 1.632 |
| Prob(Omnibus): | 0.724 | Jarque-Bera (JB): | 0.598 |
| Skew: | 0.056 | Prob(JB): | 0.742 |
| Kurtosis: | 2.028 | Cond. No. | 146. |