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 |
| 0.411 | 0.529 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 13.05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.36e-05 |
| Time: | 03:38:20 | Log-Likelihood: | -100.24 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.0071 | 159.697 | 0.276 | 0.786 | -290.243 378.257 |
| C(dose)[T.1] | -299.5090 | 345.882 | -0.866 | 0.397 | -1023.447 424.429 |
| expression | 1.0557 | 16.515 | 0.064 | 0.950 | -33.510 35.621 |
| expression:C(dose)[T.1] | 33.2828 | 33.384 | 0.997 | 0.331 | -36.591 103.156 |
| Omnibus: | 0.025 | Durbin-Watson: | 1.992 |
| Prob(Omnibus): | 0.987 | Jarque-Bera (JB): | 0.160 |
| Skew: | -0.067 | Prob(JB): | 0.923 |
| Kurtosis: | 2.614 | Cond. No. | 966. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.31e-05 |
| Time: | 03:38:20 | Log-Likelihood: | -100.83 |
| 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 | -34.6979 | 138.799 | -0.250 | 0.805 | -324.227 254.831 |
| C(dose)[T.1] | 44.9701 | 15.674 | 2.869 | 0.009 | 12.275 77.665 |
| expression | 9.2005 | 14.350 | 0.641 | 0.529 | -20.734 39.135 |
| Omnibus: | 0.073 | Durbin-Watson: | 1.895 |
| Prob(Omnibus): | 0.964 | Jarque-Bera (JB): | 0.194 |
| Skew: | 0.114 | Prob(JB): | 0.907 |
| Kurtosis: | 2.612 | Cond. No. | 329. |
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: | 03:38:20 | 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: | 0.515 |
| Model: | OLS | Adj. R-squared: | 0.491 |
| Method: | Least Squares | F-statistic: | 22.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000117 |
| Time: | 03:38:20 | Log-Likelihood: | -104.79 |
| No. Observations: | 23 | AIC: | 213.6 |
| Df Residuals: | 21 | BIC: | 215.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -359.3617 | 93.195 | -3.856 | 0.001 | -553.172 -165.552 |
| expression | 43.4813 | 9.216 | 4.718 | 0.000 | 24.317 62.646 |
| Omnibus: | 0.692 | Durbin-Watson: | 2.015 |
| Prob(Omnibus): | 0.708 | Jarque-Bera (JB): | 0.749 |
| Skew: | 0.295 | Prob(JB): | 0.688 |
| Kurtosis: | 2.342 | Cond. No. | 189. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.060 | 0.811 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.529 |
| Model: | OLS | Adj. R-squared: | 0.401 |
| Method: | Least Squares | F-statistic: | 4.118 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0348 |
| Time: | 03:38:20 | Log-Likelihood: | -69.654 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 128.3930 | 147.616 | 0.870 | 0.403 | -196.507 453.293 |
| C(dose)[T.1] | -385.2073 | 322.274 | -1.195 | 0.257 | -1094.527 324.112 |
| expression | -6.9692 | 16.827 | -0.414 | 0.687 | -44.005 30.067 |
| expression:C(dose)[T.1] | 47.7867 | 35.529 | 1.345 | 0.206 | -30.413 125.986 |
| Omnibus: | 1.759 | Durbin-Watson: | 1.136 |
| Prob(Omnibus): | 0.415 | Jarque-Bera (JB): | 1.359 |
| Skew: | -0.670 | Prob(JB): | 0.507 |
| Kurtosis: | 2.385 | Cond. No. | 465. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.939 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
| Time: | 03:38:20 | Log-Likelihood: | -70.796 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.6285 | 134.431 | 0.258 | 0.801 | -258.272 327.529 |
| C(dose)[T.1] | 47.6919 | 16.860 | 2.829 | 0.015 | 10.958 84.426 |
| expression | 3.7495 | 15.312 | 0.245 | 0.811 | -29.611 37.110 |
| Omnibus: | 2.400 | Durbin-Watson: | 0.894 |
| Prob(Omnibus): | 0.301 | Jarque-Bera (JB): | 1.735 |
| Skew: | -0.796 | Prob(JB): | 0.420 |
| Kurtosis: | 2.507 | Cond. No. | 156. |
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: | 03:38:20 | 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: | 0.086 |
| Model: | OLS | Adj. R-squared: | 0.015 |
| Method: | Least Squares | F-statistic: | 1.220 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.289 |
| Time: | 03:38:21 | Log-Likelihood: | -74.628 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -81.3794 | 158.800 | -0.512 | 0.617 | -424.447 261.688 |
| expression | 19.5326 | 17.687 | 1.104 | 0.289 | -18.677 57.742 |
| Omnibus: | 0.339 | Durbin-Watson: | 1.482 |
| Prob(Omnibus): | 0.844 | Jarque-Bera (JB): | 0.480 |
| Skew: | -0.202 | Prob(JB): | 0.787 |
| Kurtosis: | 2.222 | Cond. No. | 149. |