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 |
| 6.926 | 0.016 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.748 |
| Model: | OLS | Adj. R-squared: | 0.708 |
| Method: | Least Squares | F-statistic: | 18.81 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.50e-06 |
| Time: | 04:30:26 | Log-Likelihood: | -97.248 |
| No. Observations: | 23 | AIC: | 202.5 |
| Df Residuals: | 19 | BIC: | 207.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.3724 | 56.439 | -0.308 | 0.762 | -135.500 100.755 |
| C(dose)[T.1] | -3.5631 | 77.694 | -0.046 | 0.964 | -166.179 159.053 |
| expression | 9.9156 | 7.784 | 1.274 | 0.218 | -6.376 26.208 |
| expression:C(dose)[T.1] | 8.9983 | 11.033 | 0.816 | 0.425 | -14.093 32.090 |
| Omnibus: | 1.576 | Durbin-Watson: | 2.211 |
| Prob(Omnibus): | 0.455 | Jarque-Bera (JB): | 1.114 |
| Skew: | -0.270 | Prob(JB): | 0.573 |
| Kurtosis: | 2.067 | Cond. No. | 190. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.739 |
| Model: | OLS | Adj. R-squared: | 0.713 |
| Method: | Least Squares | F-statistic: | 28.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.45e-06 |
| Time: | 04:30:26 | Log-Likelihood: | -97.643 |
| No. Observations: | 23 | AIC: | 201.3 |
| Df Residuals: | 20 | BIC: | 204.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -49.7074 | 39.832 | -1.248 | 0.226 | -132.795 33.380 |
| C(dose)[T.1] | 59.4701 | 7.909 | 7.519 | 0.000 | 42.971 75.969 |
| expression | 14.3948 | 5.470 | 2.632 | 0.016 | 2.985 25.805 |
| Omnibus: | 5.223 | Durbin-Watson: | 2.318 |
| Prob(Omnibus): | 0.073 | Jarque-Bera (JB): | 1.747 |
| Skew: | -0.189 | Prob(JB): | 0.418 |
| Kurtosis: | 1.704 | Cond. No. | 76.2 |
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: | 04:30:26 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05206 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.822 |
| Time: | 04:30:26 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 63.7449 | 70.373 | 0.906 | 0.375 | -82.604 210.094 |
| expression | 2.2768 | 9.979 | 0.228 | 0.822 | -18.475 23.029 |
| Omnibus: | 3.543 | Durbin-Watson: | 2.524 |
| Prob(Omnibus): | 0.170 | Jarque-Bera (JB): | 1.575 |
| Skew: | 0.263 | Prob(JB): | 0.455 |
| Kurtosis: | 1.831 | Cond. No. | 70.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.197 | 0.665 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.462 |
| Model: | OLS | Adj. R-squared: | 0.316 |
| Method: | Least Squares | F-statistic: | 3.152 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0685 |
| Time: | 04:30:27 | Log-Likelihood: | -70.647 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.3884 | 80.301 | 0.603 | 0.559 | -128.352 225.129 |
| C(dose)[T.1] | -4.5035 | 175.385 | -0.026 | 0.980 | -390.524 381.517 |
| expression | 2.6084 | 10.880 | 0.240 | 0.815 | -21.339 26.556 |
| expression:C(dose)[T.1] | 7.3370 | 23.887 | 0.307 | 0.764 | -45.237 59.911 |
| Omnibus: | 2.260 | Durbin-Watson: | 0.747 |
| Prob(Omnibus): | 0.323 | Jarque-Bera (JB): | 1.643 |
| Skew: | -0.652 | Prob(JB): | 0.440 |
| Kurtosis: | 2.036 | Cond. No. | 193. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.367 |
| Method: | Least Squares | F-statistic: | 5.063 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0254 |
| Time: | 04:30:27 | Log-Likelihood: | -70.711 |
| 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 | 37.2765 | 68.932 | 0.541 | 0.599 | -112.913 187.466 |
| C(dose)[T.1] | 49.1367 | 15.613 | 3.147 | 0.008 | 15.119 83.154 |
| expression | 4.1307 | 9.313 | 0.444 | 0.665 | -16.161 24.423 |
| Omnibus: | 2.159 | Durbin-Watson: | 0.734 |
| Prob(Omnibus): | 0.340 | Jarque-Bera (JB): | 1.662 |
| Skew: | -0.740 | Prob(JB): | 0.436 |
| Kurtosis: | 2.316 | Cond. No. | 66.5 |
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: | 04:30:27 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.066 |
| Method: | Least Squares | F-statistic: | 0.1315 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.723 |
| Time: | 04:30:27 | Log-Likelihood: | -75.225 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.6357 | 88.913 | 0.693 | 0.500 | -130.449 253.720 |
| expression | 4.3835 | 12.089 | 0.363 | 0.723 | -21.733 30.500 |
| Omnibus: | 0.622 | Durbin-Watson: | 1.599 |
| Prob(Omnibus): | 0.733 | Jarque-Bera (JB): | 0.593 |
| Skew: | 0.088 | Prob(JB): | 0.743 |
| Kurtosis: | 2.042 | Cond. No. | 65.9 |