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 |
| 3.229 | 0.087 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.733 |
| Model: | OLS | Adj. R-squared: | 0.691 |
| Method: | Least Squares | F-statistic: | 17.40 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.11e-05 |
| Time: | 11:38:50 | Log-Likelihood: | -97.910 |
| No. Observations: | 23 | AIC: | 203.8 |
| Df Residuals: | 19 | BIC: | 208.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.8905 | 32.378 | 1.541 | 0.140 | -17.878 117.659 |
| C(dose)[T.1] | -12.4540 | 43.626 | -0.285 | 0.778 | -103.765 78.857 |
| expression | 1.1615 | 8.587 | 0.135 | 0.894 | -16.810 19.133 |
| expression:C(dose)[T.1] | 18.8100 | 11.853 | 1.587 | 0.129 | -5.999 43.619 |
| Omnibus: | 0.791 | Durbin-Watson: | 2.274 |
| Prob(Omnibus): | 0.673 | Jarque-Bera (JB): | 0.820 |
| Skew: | 0.339 | Prob(JB): | 0.664 |
| Kurtosis: | 2.372 | Cond. No. | 57.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.698 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 23.09 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 6.34e-06 |
| Time: | 11:38:50 | Log-Likelihood: | -99.342 |
| No. Observations: | 23 | AIC: | 204.7 |
| Df Residuals: | 20 | BIC: | 208.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.1945 | 23.508 | 0.561 | 0.581 | -35.843 62.232 |
| C(dose)[T.1] | 55.6218 | 8.236 | 6.753 | 0.000 | 38.441 72.802 |
| expression | 11.0326 | 6.140 | 1.797 | 0.087 | -1.775 23.840 |
| Omnibus: | 0.987 | Durbin-Watson: | 1.712 |
| Prob(Omnibus): | 0.610 | Jarque-Bera (JB): | 0.805 |
| Skew: | -0.134 | Prob(JB): | 0.669 |
| Kurtosis: | 2.124 | Cond. No. | 23.0 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:38:50 | 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: | 0.009 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.1866 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.670 |
| Time: | 11:38:50 | Log-Likelihood: | -113.00 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.9573 | 39.458 | 1.596 | 0.126 | -19.099 145.014 |
| expression | 4.6318 | 10.722 | 0.432 | 0.670 | -17.666 26.930 |
| Omnibus: | 2.876 | Durbin-Watson: | 2.523 |
| Prob(Omnibus): | 0.237 | Jarque-Bera (JB): | 1.356 |
| Skew: | 0.193 | Prob(JB): | 0.508 |
| Kurtosis: | 1.875 | Cond. No. | 21.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.826 | 0.202 | 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: | 4.120 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0347 |
| Time: | 11:38:50 | Log-Likelihood: | -69.652 |
| 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 | 153.8845 | 64.506 | 2.386 | 0.036 | 11.907 295.862 |
| C(dose)[T.1] | -15.9135 | 148.916 | -0.107 | 0.917 | -343.675 311.848 |
| expression | -18.6468 | 13.705 | -1.361 | 0.201 | -48.812 11.518 |
| expression:C(dose)[T.1] | 13.8565 | 33.010 | 0.420 | 0.683 | -58.798 86.511 |
| Omnibus: | 3.191 | Durbin-Watson: | 1.038 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.870 |
| Skew: | -0.865 | Prob(JB): | 0.393 |
| Kurtosis: | 2.948 | Cond. No. | 111. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.522 |
| Model: | OLS | Adj. R-squared: | 0.442 |
| Method: | Least Squares | F-statistic: | 6.541 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0120 |
| Time: | 11:38:50 | Log-Likelihood: | -69.771 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 12 | BIC: | 147.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.8099 | 56.808 | 2.514 | 0.027 | 19.036 266.584 |
| C(dose)[T.1] | 46.2628 | 14.824 | 3.121 | 0.009 | 13.965 78.560 |
| expression | -16.2582 | 12.033 | -1.351 | 0.202 | -42.475 9.959 |
| Omnibus: | 3.910 | Durbin-Watson: | 0.937 |
| Prob(Omnibus): | 0.142 | Jarque-Bera (JB): | 2.277 |
| Skew: | -0.954 | Prob(JB): | 0.320 |
| Kurtosis: | 3.062 | Cond. No. | 37.5 |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:38:51 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.067 |
| Method: | Least Squares | F-statistic: | 1.998 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.181 |
| Time: | 11:38:51 | Log-Likelihood: | -74.228 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 192.4561 | 70.524 | 2.729 | 0.017 | 40.099 344.813 |
| expression | -21.7585 | 15.393 | -1.414 | 0.181 | -55.012 11.495 |
| Omnibus: | 3.811 | Durbin-Watson: | 1.914 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 1.380 |
| Skew: | 0.269 | Prob(JB): | 0.501 |
| Kurtosis: | 1.615 | Cond. No. | 35.8 |