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 |
| 1.296 | 0.268 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.685 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 13.76 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.25e-05 |
| Time: | 22:52:14 | Log-Likelihood: | -99.825 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 19 | BIC: | 212.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.2356 | 119.488 | 0.764 | 0.455 | -158.855 341.326 |
| C(dose)[T.1] | 230.7826 | 191.329 | 1.206 | 0.243 | -169.674 631.239 |
| expression | -4.9182 | 15.852 | -0.310 | 0.760 | -38.096 28.260 |
| expression:C(dose)[T.1] | -23.8600 | 25.547 | -0.934 | 0.362 | -77.330 29.610 |
| Omnibus: | 0.189 | Durbin-Watson: | 1.836 |
| Prob(Omnibus): | 0.910 | Jarque-Bera (JB): | 0.018 |
| Skew: | 0.038 | Prob(JB): | 0.991 |
| Kurtosis: | 2.886 | Cond. No. | 423. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 20.34 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.51e-05 |
| Time: | 22:52:15 | Log-Likelihood: | -100.34 |
| No. Observations: | 23 | AIC: | 206.7 |
| Df Residuals: | 20 | BIC: | 210.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 160.3988 | 93.474 | 1.716 | 0.102 | -34.585 355.383 |
| C(dose)[T.1] | 52.2652 | 8.551 | 6.112 | 0.000 | 34.428 70.102 |
| expression | -14.1049 | 12.391 | -1.138 | 0.268 | -39.953 11.743 |
| Omnibus: | 0.159 | Durbin-Watson: | 2.029 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.157 |
| Skew: | 0.148 | Prob(JB): | 0.925 |
| Kurtosis: | 2.723 | 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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:52:15 | 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: | 0.055 |
| Model: | OLS | Adj. R-squared: | 0.010 |
| Method: | Least Squares | F-statistic: | 1.216 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.283 |
| Time: | 22:52:15 | Log-Likelihood: | -112.46 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 247.8853 | 152.663 | 1.624 | 0.119 | -69.595 565.365 |
| expression | -22.4455 | 20.355 | -1.103 | 0.283 | -64.775 19.884 |
| Omnibus: | 4.513 | Durbin-Watson: | 2.624 |
| Prob(Omnibus): | 0.105 | Jarque-Bera (JB): | 1.640 |
| Skew: | 0.187 | Prob(JB): | 0.440 |
| Kurtosis: | 1.746 | Cond. No. | 166. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.179 | 0.679 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 3.829 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0423 |
| Time: | 22:52:15 | Log-Likelihood: | -69.937 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 319.2986 | 241.862 | 1.320 | 0.214 | -213.036 851.633 |
| C(dose)[T.1] | -369.2620 | 385.167 | -0.959 | 0.358 | -1217.008 478.484 |
| expression | -32.7837 | 31.447 | -1.043 | 0.320 | -101.997 36.430 |
| expression:C(dose)[T.1] | 53.0997 | 48.204 | 1.102 | 0.294 | -52.997 159.196 |
| Omnibus: | 1.717 | Durbin-Watson: | 0.751 |
| Prob(Omnibus): | 0.424 | Jarque-Bera (JB): | 1.325 |
| Skew: | -0.570 | Prob(JB): | 0.516 |
| Kurtosis: | 2.093 | Cond. No. | 520. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.048 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0257 |
| Time: | 22:52:15 | Log-Likelihood: | -70.722 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 145.6822 | 185.081 | 0.787 | 0.446 | -257.575 548.940 |
| C(dose)[T.1] | 54.4628 | 19.966 | 2.728 | 0.018 | 10.960 97.965 |
| expression | -10.1856 | 24.045 | -0.424 | 0.679 | -62.574 42.203 |
| Omnibus: | 3.306 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.191 | Jarque-Bera (JB): | 1.903 |
| Skew: | -0.872 | Prob(JB): | 0.386 |
| Kurtosis: | 2.993 | Cond. No. | 193. |
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: | 22:52:15 | 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: | 0.120 |
| Model: | OLS | Adj. R-squared: | 0.052 |
| Method: | Least Squares | F-statistic: | 1.775 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.206 |
| Time: | 22:52:15 | Log-Likelihood: | -74.340 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -150.2915 | 183.359 | -0.820 | 0.427 | -546.414 245.831 |
| expression | 30.6536 | 23.008 | 1.332 | 0.206 | -19.052 80.360 |
| Omnibus: | 0.609 | Durbin-Watson: | 1.211 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.482 |
| Skew: | -0.382 | Prob(JB): | 0.786 |
| Kurtosis: | 2.565 | Cond. No. | 156. |