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 |
| 0.382 | 0.543 | 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: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.37 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000102 |
| Time: | 04:47:28 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.2401 | 98.836 | 1.378 | 0.184 | -70.627 343.107 |
| C(dose)[T.1] | -23.4446 | 130.506 | -0.180 | 0.859 | -296.597 249.708 |
| expression | -11.4815 | 13.807 | -0.832 | 0.416 | -40.380 17.417 |
| expression:C(dose)[T.1] | 10.6983 | 18.745 | 0.571 | 0.575 | -28.536 49.933 |
| Omnibus: | 0.096 | Durbin-Watson: | 1.797 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.322 |
| Skew: | 0.014 | Prob(JB): | 0.851 |
| Kurtosis: | 2.421 | Cond. No. | 277. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.04 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.34e-05 |
| Time: | 04:47:28 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 94.7718 | 65.862 | 1.439 | 0.166 | -42.614 232.158 |
| C(dose)[T.1] | 50.8293 | 9.587 | 5.302 | 0.000 | 30.831 70.827 |
| expression | -5.6774 | 9.180 | -0.618 | 0.543 | -24.826 13.472 |
| Omnibus: | 0.199 | Durbin-Watson: | 1.884 |
| Prob(Omnibus): | 0.905 | Jarque-Bera (JB): | 0.400 |
| Skew: | 0.109 | Prob(JB): | 0.819 |
| Kurtosis: | 2.391 | Cond. No. | 108. |
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: | 04:47:28 | 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: | 0.172 |
| Model: | OLS | Adj. R-squared: | 0.132 |
| Method: | Least Squares | F-statistic: | 4.351 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0494 |
| Time: | 04:47:28 | Log-Likelihood: | -110.94 |
| No. Observations: | 23 | AIC: | 225.9 |
| Df Residuals: | 21 | BIC: | 228.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 261.8152 | 87.543 | 2.991 | 0.007 | 79.761 443.870 |
| expression | -26.2638 | 12.591 | -2.086 | 0.049 | -52.447 -0.080 |
| Omnibus: | 1.337 | Durbin-Watson: | 2.418 |
| Prob(Omnibus): | 0.512 | Jarque-Bera (JB): | 1.146 |
| Skew: | 0.501 | Prob(JB): | 0.564 |
| Kurtosis: | 2.564 | Cond. No. | 94.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.144 | 0.711 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.312 |
| Method: | Least Squares | F-statistic: | 3.116 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0704 |
| Time: | 04:47:28 | Log-Likelihood: | -70.687 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.9481 | 162.700 | 0.307 | 0.765 | -308.152 408.048 |
| C(dose)[T.1] | -26.7781 | 263.425 | -0.102 | 0.921 | -606.572 553.016 |
| expression | 2.1699 | 20.143 | 0.108 | 0.916 | -42.164 46.504 |
| expression:C(dose)[T.1] | 9.2944 | 32.400 | 0.287 | 0.780 | -62.018 80.606 |
| Omnibus: | 2.449 | Durbin-Watson: | 0.902 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.473 |
| Skew: | -0.762 | Prob(JB): | 0.479 |
| Kurtosis: | 2.823 | Cond. No. | 338. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.016 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0261 |
| Time: | 04:47:28 | Log-Likelihood: | -70.743 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 21.0100 | 122.673 | 0.171 | 0.867 | -246.272 288.292 |
| C(dose)[T.1] | 48.6431 | 15.713 | 3.096 | 0.009 | 14.407 82.880 |
| expression | 5.7621 | 15.162 | 0.380 | 0.711 | -27.273 38.797 |
| Omnibus: | 2.698 | Durbin-Watson: | 0.825 |
| Prob(Omnibus): | 0.260 | Jarque-Bera (JB): | 1.744 |
| Skew: | -0.825 | Prob(JB): | 0.418 |
| Kurtosis: | 2.734 | Cond. No. | 130. |
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: | 04:47:28 | 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: | 0.020 |
| Model: | OLS | Adj. R-squared: | -0.055 |
| Method: | Least Squares | F-statistic: | 0.2702 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.612 |
| Time: | 04:47:28 | Log-Likelihood: | -75.146 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.6955 | 158.017 | 0.074 | 0.942 | -329.680 353.071 |
| expression | 10.1112 | 19.452 | 0.520 | 0.612 | -31.912 52.134 |
| Omnibus: | 0.167 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.920 | Jarque-Bera (JB): | 0.375 |
| Skew: | 0.036 | Prob(JB): | 0.829 |
| Kurtosis: | 2.229 | Cond. No. | 130. |