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 |
| 0.046 | 0.832 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 12.38 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000102 |
| Time: | 03:39:52 | Log-Likelihood: | -100.64 |
| 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 | 34.4580 | 29.002 | 1.188 | 0.249 | -26.244 95.160 |
| C(dose)[T.1] | 89.9843 | 46.751 | 1.925 | 0.069 | -7.867 187.835 |
| expression | 4.4053 | 6.324 | 0.697 | 0.494 | -8.830 17.641 |
| expression:C(dose)[T.1] | -7.7617 | 9.562 | -0.812 | 0.427 | -27.776 12.252 |
| Omnibus: | 0.608 | Durbin-Watson: | 1.577 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.630 |
| Skew: | 0.060 | Prob(JB): | 0.730 |
| Kurtosis: | 2.198 | Cond. No. | 67.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.56 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.77e-05 |
| Time: | 03:39:52 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.6767 | 21.937 | 2.265 | 0.035 | 3.917 95.437 |
| C(dose)[T.1] | 52.7803 | 9.135 | 5.778 | 0.000 | 33.726 71.835 |
| expression | 1.0108 | 4.703 | 0.215 | 0.832 | -8.799 10.821 |
| Omnibus: | 0.333 | Durbin-Watson: | 1.877 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.491 |
| Skew: | 0.049 | Prob(JB): | 0.782 |
| Kurtosis: | 2.291 | Cond. No. | 25.5 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 03:39:52 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | 0.021 |
| Method: | Least Squares | F-statistic: | 1.470 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.239 |
| Time: | 03:39:52 | Log-Likelihood: | -112.33 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 38.3415 | 34.837 | 1.101 | 0.284 | -34.105 110.788 |
| expression | 8.7166 | 7.190 | 1.212 | 0.239 | -6.236 23.670 |
| Omnibus: | 2.733 | Durbin-Watson: | 2.444 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.584 |
| Skew: | 0.369 | Prob(JB): | 0.453 |
| Kurtosis: | 1.947 | Cond. No. | 25.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.969 | 0.186 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.532 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 4.167 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0337 |
| Time: | 03:39:52 | Log-Likelihood: | -69.606 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 11 | BIC: | 150.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.0456 | 168.755 | 1.126 | 0.284 | -181.383 561.474 |
| C(dose)[T.1] | 130.8771 | 240.594 | 0.544 | 0.597 | -398.666 660.420 |
| expression | -31.8880 | 43.792 | -0.728 | 0.482 | -128.275 64.499 |
| expression:C(dose)[T.1] | -22.6792 | 63.309 | -0.358 | 0.727 | -162.022 116.663 |
| Omnibus: | 1.094 | Durbin-Watson: | 0.788 |
| Prob(Omnibus): | 0.579 | Jarque-Bera (JB): | 0.842 |
| Skew: | -0.292 | Prob(JB): | 0.656 |
| Kurtosis: | 1.997 | Cond. No. | 170. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.526 |
| Model: | OLS | Adj. R-squared: | 0.448 |
| Method: | Least Squares | F-statistic: | 6.671 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0113 |
| Time: | 03:39:52 | Log-Likelihood: | -69.693 |
| No. Observations: | 15 | AIC: | 145.4 |
| Df Residuals: | 12 | BIC: | 147.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 231.7726 | 117.590 | 1.971 | 0.072 | -24.435 487.980 |
| C(dose)[T.1] | 44.8679 | 14.911 | 3.009 | 0.011 | 12.381 77.355 |
| expression | -42.7396 | 30.455 | -1.403 | 0.186 | -109.095 23.616 |
| Omnibus: | 1.482 | Durbin-Watson: | 0.737 |
| Prob(Omnibus): | 0.477 | Jarque-Bera (JB): | 0.969 |
| Skew: | -0.306 | Prob(JB): | 0.616 |
| Kurtosis: | 1.915 | Cond. No. | 66.2 |
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: | 03:39:52 | 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: | 0.169 |
| Model: | OLS | Adj. R-squared: | 0.105 |
| Method: | Least Squares | F-statistic: | 2.647 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.128 |
| Time: | 03:39:52 | Log-Likelihood: | -73.910 |
| No. Observations: | 15 | AIC: | 151.8 |
| Df Residuals: | 13 | BIC: | 153.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 327.5738 | 144.061 | 2.274 | 0.041 | 16.349 638.798 |
| expression | -61.6970 | 37.920 | -1.627 | 0.128 | -143.618 20.224 |
| Omnibus: | 1.090 | Durbin-Watson: | 1.381 |
| Prob(Omnibus): | 0.580 | Jarque-Bera (JB): | 0.841 |
| Skew: | 0.292 | Prob(JB): | 0.657 |
| Kurtosis: | 1.998 | Cond. No. | 63.2 |