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 |
| 0.539 | 0.471 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000111 |
| Time: | 05:20:28 | Log-Likelihood: | -100.75 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 169.6191 | 239.392 | 0.709 | 0.487 | -331.434 670.672 |
| C(dose)[T.1] | 25.2965 | 289.468 | 0.087 | 0.931 | -580.568 631.161 |
| expression | -13.5182 | 28.031 | -0.482 | 0.635 | -72.188 45.152 |
| expression:C(dose)[T.1] | 3.1465 | 34.035 | 0.092 | 0.927 | -68.089 74.382 |
| Omnibus: | 0.232 | Durbin-Watson: | 2.077 |
| Prob(Omnibus): | 0.891 | Jarque-Bera (JB): | 0.428 |
| Skew: | 0.042 | Prob(JB): | 0.807 |
| Kurtosis: | 2.337 | Cond. No. | 789. |
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: | 19.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.17e-05 |
| Time: | 05:20:28 | Log-Likelihood: | -100.76 |
| 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 | 151.3974 | 132.460 | 1.143 | 0.267 | -124.909 427.703 |
| C(dose)[T.1] | 52.0446 | 8.831 | 5.893 | 0.000 | 33.623 70.466 |
| expression | -11.3839 | 15.499 | -0.734 | 0.471 | -43.715 20.947 |
| Omnibus: | 0.245 | Durbin-Watson: | 2.054 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.436 |
| Skew: | 0.036 | Prob(JB): | 0.804 |
| Kurtosis: | 2.329 | Cond. No. | 264. |
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: | 05:20:28 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | 0.020 |
| Method: | Least Squares | F-statistic: | 1.456 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.241 |
| Time: | 05:20:28 | Log-Likelihood: | -112.33 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 330.7041 | 208.125 | 1.589 | 0.127 | -102.115 763.524 |
| expression | -29.5867 | 24.520 | -1.207 | 0.241 | -80.579 21.406 |
| Omnibus: | 4.413 | Durbin-Watson: | 2.405 |
| Prob(Omnibus): | 0.110 | Jarque-Bera (JB): | 1.757 |
| Skew: | 0.282 | Prob(JB): | 0.415 |
| Kurtosis: | 1.769 | Cond. No. | 257. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.198 | 0.664 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.479 |
| Model: | OLS | Adj. R-squared: | 0.337 |
| Method: | Least Squares | F-statistic: | 3.372 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0583 |
| Time: | 05:20:28 | Log-Likelihood: | -70.409 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.0713 | 336.424 | 0.289 | 0.778 | -643.393 837.536 |
| C(dose)[T.1] | -301.7609 | 525.803 | -0.574 | 0.578 | -1459.045 855.523 |
| expression | -3.5584 | 40.361 | -0.088 | 0.931 | -92.392 85.275 |
| expression:C(dose)[T.1] | 42.8665 | 63.804 | 0.672 | 0.516 | -97.565 183.298 |
| Omnibus: | 4.604 | Durbin-Watson: | 0.727 |
| Prob(Omnibus): | 0.100 | Jarque-Bera (JB): | 2.853 |
| Skew: | -1.068 | Prob(JB): | 0.240 |
| Kurtosis: | 3.036 | Cond. No. | 702. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 5.065 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0254 |
| Time: | 05:20:28 | Log-Likelihood: | -70.710 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -45.8202 | 254.637 | -0.180 | 0.860 | -600.627 508.986 |
| C(dose)[T.1] | 51.3197 | 16.324 | 3.144 | 0.008 | 15.754 86.886 |
| expression | 13.5946 | 30.536 | 0.445 | 0.664 | -52.939 80.128 |
| Omnibus: | 3.836 | Durbin-Watson: | 0.768 |
| Prob(Omnibus): | 0.147 | Jarque-Bera (JB): | 2.517 |
| Skew: | -0.999 | Prob(JB): | 0.284 |
| Kurtosis: | 2.811 | Cond. No. | 274. |
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: | 05:20:28 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.065 |
| Method: | Least Squares | F-statistic: | 0.1456 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.709 |
| Time: | 05:20:28 | Log-Likelihood: | -75.217 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 212.8856 | 312.654 | 0.681 | 0.508 | -462.563 888.334 |
| expression | -14.4558 | 37.891 | -0.382 | 0.709 | -96.314 67.402 |
| Omnibus: | 0.836 | Durbin-Watson: | 1.546 |
| Prob(Omnibus): | 0.658 | Jarque-Bera (JB): | 0.680 |
| Skew: | 0.137 | Prob(JB): | 0.712 |
| Kurtosis: | 1.994 | Cond. No. | 259. |