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 |
| 0.439 | 0.515 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000104 |
| Time: | 04:58:03 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.4006 | 126.678 | -0.366 | 0.718 | -311.540 218.739 |
| C(dose)[T.1] | 134.2450 | 168.038 | 0.799 | 0.434 | -217.462 485.953 |
| expression | 14.3904 | 18.098 | 0.795 | 0.436 | -23.489 52.270 |
| expression:C(dose)[T.1] | -11.6531 | 23.708 | -0.492 | 0.629 | -61.275 37.968 |
| Omnibus: | 0.658 | Durbin-Watson: | 2.090 |
| Prob(Omnibus): | 0.720 | Jarque-Bera (JB): | 0.661 |
| Skew: | -0.102 | Prob(JB): | 0.718 |
| Kurtosis: | 2.195 | Cond. No. | 371. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.28e-05 |
| Time: | 04:58:03 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.0755 | 80.393 | 0.013 | 0.989 | -166.621 168.772 |
| C(dose)[T.1] | 51.7730 | 8.990 | 5.759 | 0.000 | 33.019 70.527 |
| expression | 7.5998 | 11.467 | 0.663 | 0.515 | -16.320 31.519 |
| Omnibus: | 0.278 | Durbin-Watson: | 2.014 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.457 |
| Skew: | -0.146 | Prob(JB): | 0.796 |
| Kurtosis: | 2.374 | Cond. No. | 135. |
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: | 04:58:03 | 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: | 0.087 |
| Model: | OLS | Adj. R-squared: | 0.044 |
| Method: | Least Squares | F-statistic: | 2.006 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.171 |
| Time: | 04:58:03 | Log-Likelihood: | -112.06 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -97.0588 | 125.006 | -0.776 | 0.446 | -357.022 162.905 |
| expression | 24.9338 | 17.605 | 1.416 | 0.171 | -11.678 61.545 |
| Omnibus: | 4.575 | Durbin-Watson: | 2.686 |
| Prob(Omnibus): | 0.102 | Jarque-Bera (JB): | 1.691 |
| Skew: | 0.221 | Prob(JB): | 0.429 |
| Kurtosis: | 1.747 | Cond. No. | 131. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.241 | 0.633 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.483 |
| Model: | OLS | Adj. R-squared: | 0.342 |
| Method: | Least Squares | F-statistic: | 3.423 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0562 |
| Time: | 04:58:03 | Log-Likelihood: | -70.354 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.8427 | 277.180 | 0.093 | 0.927 | -584.226 635.911 |
| C(dose)[T.1] | 325.2510 | 394.008 | 0.825 | 0.427 | -541.955 1192.457 |
| expression | 6.5312 | 43.494 | 0.150 | 0.883 | -89.197 102.260 |
| expression:C(dose)[T.1] | -43.6419 | 62.073 | -0.703 | 0.497 | -180.264 92.980 |
| Omnibus: | 2.710 | Durbin-Watson: | 0.759 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.929 |
| Skew: | -0.849 | Prob(JB): | 0.381 |
| Kurtosis: | 2.546 | Cond. No. | 427. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 5.103 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0249 |
| Time: | 04:58:03 | Log-Likelihood: | -70.684 |
| 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 | 162.2691 | 193.713 | 0.838 | 0.419 | -259.796 584.334 |
| C(dose)[T.1] | 48.4636 | 15.656 | 3.096 | 0.009 | 14.353 82.574 |
| expression | -14.8950 | 30.371 | -0.490 | 0.633 | -81.067 51.277 |
| Omnibus: | 2.609 | Durbin-Watson: | 0.846 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.895 |
| Skew: | -0.833 | Prob(JB): | 0.388 |
| Kurtosis: | 2.494 | Cond. No. | 163. |
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: | 04:58:04 | 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: | 0.028 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.3754 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.551 |
| Time: | 04:58:04 | Log-Likelihood: | -75.087 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 245.0144 | 247.210 | 0.991 | 0.340 | -289.050 779.079 |
| expression | -23.8679 | 38.954 | -0.613 | 0.551 | -108.022 60.286 |
| Omnibus: | 0.826 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.662 | Jarque-Bera (JB): | 0.656 |
| Skew: | -0.033 | Prob(JB): | 0.720 |
| Kurtosis: | 1.978 | Cond. No. | 160. |