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 |
| 0.248 | 0.624 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 14.27 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.16e-05 |
| Time: | 22:50:42 | Log-Likelihood: | -99.536 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -31.8727 | 68.385 | -0.466 | 0.646 | -175.005 111.260 |
| C(dose)[T.1] | 252.9290 | 128.130 | 1.974 | 0.063 | -15.249 521.107 |
| expression | 13.4727 | 10.664 | 1.263 | 0.222 | -8.848 35.793 |
| expression:C(dose)[T.1] | -31.3955 | 20.137 | -1.559 | 0.135 | -73.543 10.752 |
| Omnibus: | 0.282 | Durbin-Watson: | 1.748 |
| Prob(Omnibus): | 0.868 | Jarque-Bera (JB): | 0.460 |
| Skew: | -0.031 | Prob(JB): | 0.795 |
| Kurtosis: | 2.310 | Cond. No. | 236. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.85 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.50e-05 |
| Time: | 22:50:42 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.3851 | 60.132 | 0.406 | 0.689 | -101.049 149.819 |
| C(dose)[T.1] | 53.5984 | 8.732 | 6.138 | 0.000 | 35.385 71.812 |
| expression | 4.6677 | 9.364 | 0.498 | 0.624 | -14.865 24.201 |
| Omnibus: | 0.419 | Durbin-Watson: | 1.959 |
| Prob(Omnibus): | 0.811 | Jarque-Bera (JB): | 0.536 |
| Skew: | 0.015 | Prob(JB): | 0.765 |
| Kurtosis: | 2.253 | Cond. No. | 90.5 |
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: | 22:50:42 | 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: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.006176 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.938 |
| Time: | 22:50:43 | 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 | 71.9714 | 98.827 | 0.728 | 0.474 | -133.550 277.492 |
| expression | 1.2174 | 15.491 | 0.079 | 0.938 | -30.998 33.433 |
| Omnibus: | 3.331 | Durbin-Watson: | 2.483 |
| Prob(Omnibus): | 0.189 | Jarque-Bera (JB): | 1.599 |
| Skew: | 0.304 | Prob(JB): | 0.450 |
| Kurtosis: | 1.860 | Cond. No. | 89.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.597 | 0.133 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.551 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 4.506 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0270 |
| Time: | 22:50:43 | Log-Likelihood: | -69.289 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -102.8644 | 126.366 | -0.814 | 0.433 | -380.994 175.265 |
| C(dose)[T.1] | -77.6972 | 380.729 | -0.204 | 0.842 | -915.675 760.281 |
| expression | 26.4506 | 19.555 | 1.353 | 0.203 | -16.591 69.492 |
| expression:C(dose)[T.1] | 19.6166 | 58.985 | 0.333 | 0.746 | -110.209 149.443 |
| Omnibus: | 7.629 | Durbin-Watson: | 0.945 |
| Prob(Omnibus): | 0.022 | Jarque-Bera (JB): | 4.199 |
| Skew: | -1.127 | Prob(JB): | 0.123 |
| Kurtosis: | 4.281 | Cond. No. | 398. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.547 |
| Model: | OLS | Adj. R-squared: | 0.471 |
| Method: | Least Squares | F-statistic: | 7.240 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00866 |
| Time: | 22:50:43 | Log-Likelihood: | -69.364 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 12 | BIC: | 146.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -116.7457 | 114.768 | -1.017 | 0.329 | -366.804 133.313 |
| C(dose)[T.1] | 48.8247 | 14.273 | 3.421 | 0.005 | 17.726 79.923 |
| expression | 28.6067 | 17.753 | 1.611 | 0.133 | -10.073 67.286 |
| Omnibus: | 5.377 | Durbin-Watson: | 0.974 |
| Prob(Omnibus): | 0.068 | Jarque-Bera (JB): | 2.729 |
| Skew: | -0.983 | Prob(JB): | 0.255 |
| Kurtosis: | 3.708 | Cond. No. | 107. |
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: | 22:50:43 | 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: | 0.105 |
| Model: | OLS | Adj. R-squared: | 0.036 |
| Method: | Least Squares | F-statistic: | 1.524 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.239 |
| Time: | 22:50:43 | Log-Likelihood: | -74.469 |
| No. Observations: | 15 | AIC: | 152.9 |
| Df Residuals: | 13 | BIC: | 154.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -97.0316 | 154.771 | -0.627 | 0.542 | -431.395 237.332 |
| expression | 29.5882 | 23.967 | 1.235 | 0.239 | -22.190 81.367 |
| Omnibus: | 2.977 | Durbin-Watson: | 2.007 |
| Prob(Omnibus): | 0.226 | Jarque-Bera (JB): | 1.221 |
| Skew: | 0.237 | Prob(JB): | 0.543 |
| Kurtosis: | 1.685 | Cond. No. | 106. |