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 |
| 0.105 | 0.749 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.710 |
| Model: | OLS | Adj. R-squared: | 0.664 |
| Method: | Least Squares | F-statistic: | 15.47 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.46e-05 |
| Time: | 04:10:17 | Log-Likelihood: | -98.885 |
| No. Observations: | 23 | AIC: | 205.8 |
| Df Residuals: | 19 | BIC: | 210.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -47.4896 | 89.982 | -0.528 | 0.604 | -235.823 140.844 |
| C(dose)[T.1] | 296.0007 | 124.709 | 2.374 | 0.028 | 34.981 557.021 |
| expression | 16.9700 | 14.985 | 1.132 | 0.272 | -14.394 48.334 |
| expression:C(dose)[T.1] | -41.7372 | 21.299 | -1.960 | 0.065 | -86.316 2.841 |
| Omnibus: | 1.766 | Durbin-Watson: | 2.089 |
| Prob(Omnibus): | 0.414 | Jarque-Bera (JB): | 1.020 |
| Skew: | -0.081 | Prob(JB): | 0.600 |
| Kurtosis: | 1.981 | Cond. No. | 238. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.69e-05 |
| Time: | 04:10:17 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.3261 | 68.465 | 1.115 | 0.278 | -66.488 219.141 |
| C(dose)[T.1] | 52.2256 | 9.394 | 5.559 | 0.000 | 32.629 71.822 |
| expression | -3.6907 | 11.380 | -0.324 | 0.749 | -27.429 20.047 |
| Omnibus: | 0.441 | Durbin-Watson: | 1.828 |
| Prob(Omnibus): | 0.802 | Jarque-Bera (JB): | 0.567 |
| Skew: | 0.158 | Prob(JB): | 0.753 |
| Kurtosis: | 2.298 | Cond. No. | 94.9 |
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: | 04:10:17 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.069 |
| Method: | Least Squares | F-statistic: | 2.633 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.120 |
| Time: | 04:10:17 | Log-Likelihood: | -111.75 |
| No. Observations: | 23 | AIC: | 227.5 |
| Df Residuals: | 21 | BIC: | 229.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 236.2917 | 96.723 | 2.443 | 0.023 | 35.144 437.439 |
| expression | -26.7705 | 16.496 | -1.623 | 0.120 | -61.077 7.536 |
| Omnibus: | 3.505 | Durbin-Watson: | 2.479 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 1.384 |
| Skew: | 0.065 | Prob(JB): | 0.500 |
| Kurtosis: | 1.805 | Cond. No. | 85.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.386 | 0.546 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.504 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 3.723 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0455 |
| Time: | 04:10:17 | Log-Likelihood: | -70.044 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -63.3818 | 121.508 | -0.522 | 0.612 | -330.819 204.056 |
| C(dose)[T.1] | 208.1788 | 173.875 | 1.197 | 0.256 | -174.517 590.874 |
| expression | 21.7188 | 20.085 | 1.081 | 0.303 | -22.489 65.927 |
| expression:C(dose)[T.1] | -26.4179 | 28.821 | -0.917 | 0.379 | -89.852 37.016 |
| Omnibus: | 2.486 | Durbin-Watson: | 0.896 |
| Prob(Omnibus): | 0.289 | Jarque-Bera (JB): | 1.541 |
| Skew: | -0.778 | Prob(JB): | 0.463 |
| Kurtosis: | 2.783 | Cond. No. | 183. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 5.235 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0232 |
| Time: | 04:10:17 | Log-Likelihood: | -70.596 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.8971 | 86.917 | 0.160 | 0.876 | -175.478 203.272 |
| C(dose)[T.1] | 49.4428 | 15.498 | 3.190 | 0.008 | 15.676 83.209 |
| expression | 8.8880 | 14.308 | 0.621 | 0.546 | -22.287 40.063 |
| Omnibus: | 2.413 | Durbin-Watson: | 0.755 |
| Prob(Omnibus): | 0.299 | Jarque-Bera (JB): | 1.735 |
| Skew: | -0.798 | Prob(JB): | 0.420 |
| Kurtosis: | 2.519 | Cond. No. | 69.9 |
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: | 04:10:17 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.063 |
| Method: | Least Squares | F-statistic: | 0.1707 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.686 |
| Time: | 04:10:17 | Log-Likelihood: | -75.202 |
| 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 | 47.2853 | 112.700 | 0.420 | 0.682 | -196.188 290.759 |
| expression | 7.7198 | 18.683 | 0.413 | 0.686 | -32.641 48.081 |
| Omnibus: | 1.229 | Durbin-Watson: | 1.597 |
| Prob(Omnibus): | 0.541 | Jarque-Bera (JB): | 0.811 |
| Skew: | 0.171 | Prob(JB): | 0.667 |
| Kurtosis: | 1.914 | Cond. No. | 69.2 |