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 |
| 7.027 | 0.015 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.788 |
| Model: | OLS | Adj. R-squared: | 0.755 |
| Method: | Least Squares | F-statistic: | 23.58 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.28e-06 |
| Time: | 22:56:42 | Log-Likelihood: | -95.252 |
| No. Observations: | 23 | AIC: | 198.5 |
| Df Residuals: | 19 | BIC: | 203.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 238.8855 | 148.218 | 1.612 | 0.124 | -71.339 549.110 |
| C(dose)[T.1] | 627.2624 | 273.159 | 2.296 | 0.033 | 55.534 1198.991 |
| expression | -19.1557 | 15.366 | -1.247 | 0.228 | -51.317 13.005 |
| expression:C(dose)[T.1] | -57.8994 | 27.911 | -2.074 | 0.052 | -116.318 0.519 |
| Omnibus: | 0.547 | Durbin-Watson: | 1.976 |
| Prob(Omnibus): | 0.761 | Jarque-Bera (JB): | 0.585 |
| Skew: | 0.312 | Prob(JB): | 0.746 |
| Kurtosis: | 2.530 | Cond. No. | 922. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.740 |
| Model: | OLS | Adj. R-squared: | 0.714 |
| Method: | Least Squares | F-statistic: | 28.51 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.40e-06 |
| Time: | 22:56:42 | Log-Likelihood: | -97.600 |
| No. Observations: | 23 | AIC: | 201.2 |
| Df Residuals: | 20 | BIC: | 204.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 408.0635 | 133.594 | 3.055 | 0.006 | 129.391 686.736 |
| C(dose)[T.1] | 60.8273 | 8.056 | 7.551 | 0.000 | 44.023 77.632 |
| expression | -36.7037 | 13.846 | -2.651 | 0.015 | -65.587 -7.820 |
| Omnibus: | 0.928 | Durbin-Watson: | 2.012 |
| Prob(Omnibus): | 0.629 | Jarque-Bera (JB): | 0.769 |
| Skew: | 0.100 | Prob(JB): | 0.681 |
| Kurtosis: | 2.127 | Cond. No. | 350. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:56:42 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 1.752e-06 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.999 |
| Time: | 22:56:43 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.0375 | 241.926 | 0.331 | 0.744 | -423.075 583.150 |
| expression | -0.0329 | 24.831 | -0.001 | 0.999 | -51.672 51.607 |
| Omnibus: | 3.315 | Durbin-Watson: | 2.489 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.572 |
| Skew: | 0.289 | Prob(JB): | 0.456 |
| Kurtosis: | 1.857 | Cond. No. | 330. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.166 | 0.301 | 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: | 3.733 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0452 |
| Time: | 22:56:43 | Log-Likelihood: | -70.034 |
| 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 | 317.0001 | 247.669 | 1.280 | 0.227 | -228.117 862.117 |
| C(dose)[T.1] | -89.8582 | 342.211 | -0.263 | 0.798 | -843.061 663.344 |
| expression | -28.6093 | 28.361 | -1.009 | 0.335 | -91.032 33.813 |
| expression:C(dose)[T.1] | 15.5519 | 39.765 | 0.391 | 0.703 | -71.970 103.074 |
| Omnibus: | 2.403 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.301 | Jarque-Bera (JB): | 1.621 |
| Skew: | -0.785 | Prob(JB): | 0.445 |
| Kurtosis: | 2.644 | Cond. No. | 511. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.498 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 5.943 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0161 |
| Time: | 22:56:43 | Log-Likelihood: | -70.137 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 247.9889 | 167.545 | 1.480 | 0.165 | -117.060 613.038 |
| C(dose)[T.1] | 43.8248 | 15.828 | 2.769 | 0.017 | 9.338 78.311 |
| expression | -20.6983 | 19.165 | -1.080 | 0.301 | -62.455 21.059 |
| Omnibus: | 2.222 | Durbin-Watson: | 0.755 |
| Prob(Omnibus): | 0.329 | Jarque-Bera (JB): | 1.506 |
| Skew: | -0.754 | Prob(JB): | 0.471 |
| Kurtosis: | 2.629 | Cond. No. | 195. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:56:43 | 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: | 0.177 |
| Model: | OLS | Adj. R-squared: | 0.113 |
| Method: | Least Squares | F-statistic: | 2.789 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.119 |
| Time: | 22:56:43 | Log-Likelihood: | -73.842 |
| No. Observations: | 15 | AIC: | 151.7 |
| Df Residuals: | 13 | BIC: | 153.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 414.5144 | 192.338 | 2.155 | 0.050 | -1.006 830.035 |
| expression | -37.3729 | 22.378 | -1.670 | 0.119 | -85.718 10.972 |
| Omnibus: | 0.746 | Durbin-Watson: | 1.323 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.731 |
| Skew: | 0.351 | Prob(JB): | 0.694 |
| Kurtosis: | 2.178 | Cond. No. | 182. |