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 |
| 0.616 | 0.442 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000106 |
| Time: | 04:47:16 | Log-Likelihood: | -100.69 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.9056 | 85.036 | 0.834 | 0.415 | -107.076 248.887 |
| C(dose)[T.1] | 70.9439 | 96.448 | 0.736 | 0.471 | -130.923 272.811 |
| expression | -2.9299 | 14.882 | -0.197 | 0.846 | -34.079 28.220 |
| expression:C(dose)[T.1] | -3.0374 | 16.820 | -0.181 | 0.859 | -38.243 32.168 |
| Omnibus: | 0.151 | Durbin-Watson: | 2.024 |
| Prob(Omnibus): | 0.927 | Jarque-Bera (JB): | 0.153 |
| Skew: | 0.143 | Prob(JB): | 0.926 |
| Kurtosis: | 2.721 | Cond. No. | 190. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.37 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.09e-05 |
| Time: | 04:47:16 | Log-Likelihood: | -100.71 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 84.4570 | 39.014 | 2.165 | 0.043 | 3.075 165.839 |
| C(dose)[T.1] | 53.6011 | 8.644 | 6.201 | 0.000 | 35.569 71.633 |
| expression | -5.3077 | 6.765 | -0.785 | 0.442 | -19.420 8.804 |
| Omnibus: | 0.088 | Durbin-Watson: | 2.011 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.173 |
| Skew: | 0.120 | Prob(JB): | 0.917 |
| Kurtosis: | 2.650 | Cond. No. | 53.8 |
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: | 04:47:16 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1062 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.748 |
| Time: | 04:47:16 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.7498 | 64.940 | 1.551 | 0.136 | -34.300 235.799 |
| expression | -3.6752 | 11.278 | -0.326 | 0.748 | -27.129 19.778 |
| Omnibus: | 2.746 | Durbin-Watson: | 2.559 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.436 |
| Skew: | 0.279 | Prob(JB): | 0.488 |
| Kurtosis: | 1.910 | Cond. No. | 53.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.013 | 0.911 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.300 |
| Method: | Least Squares | F-statistic: | 3.003 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0767 |
| Time: | 04:47:16 | Log-Likelihood: | -70.813 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 63.1456 | 133.728 | 0.472 | 0.646 | -231.187 357.478 |
| C(dose)[T.1] | 74.0676 | 180.979 | 0.409 | 0.690 | -324.265 472.400 |
| expression | 0.7344 | 22.838 | 0.032 | 0.975 | -49.532 51.001 |
| expression:C(dose)[T.1] | -3.9603 | 29.725 | -0.133 | 0.896 | -69.384 61.464 |
| Omnibus: | 2.816 | Durbin-Watson: | 0.785 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.905 |
| Skew: | -0.856 | Prob(JB): | 0.386 |
| Kurtosis: | 2.660 | Cond. No. | 193. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.897 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0279 |
| Time: | 04:47:16 | Log-Likelihood: | -70.825 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.7794 | 82.491 | 0.931 | 0.370 | -102.952 256.511 |
| C(dose)[T.1] | 50.0788 | 17.518 | 2.859 | 0.014 | 11.911 88.247 |
| expression | -1.6034 | 14.007 | -0.114 | 0.911 | -32.122 28.915 |
| Omnibus: | 2.741 | Durbin-Watson: | 0.786 |
| Prob(Omnibus): | 0.254 | Jarque-Bera (JB): | 1.886 |
| Skew: | -0.848 | Prob(JB): | 0.389 |
| Kurtosis: | 2.622 | Cond. No. | 66.9 |
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: | 04:47:16 | 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: | 0.074 |
| Model: | OLS | Adj. R-squared: | 0.003 |
| Method: | Least Squares | F-statistic: | 1.045 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.325 |
| Time: | 04:47:16 | Log-Likelihood: | -74.720 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -4.4308 | 96.471 | -0.046 | 0.964 | -212.845 203.983 |
| expression | 16.0149 | 15.668 | 1.022 | 0.325 | -17.835 49.864 |
| Omnibus: | 0.201 | Durbin-Watson: | 1.520 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.395 |
| Skew: | 0.107 | Prob(JB): | 0.821 |
| Kurtosis: | 2.234 | Cond. No. | 62.3 |