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 |
| 1.406 | 0.250 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.691 |
| Model: | OLS | Adj. R-squared: | 0.642 |
| Method: | Least Squares | F-statistic: | 14.14 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.42e-05 |
| Time: | 04:47:16 | Log-Likelihood: | -99.611 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 19 | BIC: | 211.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.2148 | 35.233 | 0.006 | 0.995 | -73.530 73.959 |
| C(dose)[T.1] | 130.6886 | 71.632 | 1.824 | 0.084 | -19.239 280.616 |
| expression | 10.9439 | 7.043 | 1.554 | 0.137 | -3.796 25.684 |
| expression:C(dose)[T.1] | -15.8667 | 14.856 | -1.068 | 0.299 | -46.962 15.228 |
| Omnibus: | 0.601 | Durbin-Watson: | 1.287 |
| Prob(Omnibus): | 0.741 | Jarque-Bera (JB): | 0.685 |
| Skew: | -0.276 | Prob(JB): | 0.710 |
| Kurtosis: | 2.360 | Cond. No. | 100. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 20.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.44e-05 |
| Time: | 04:47:16 | Log-Likelihood: | -100.28 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.8057 | 31.256 | 0.570 | 0.575 | -47.393 83.004 |
| C(dose)[T.1] | 54.7298 | 8.558 | 6.395 | 0.000 | 36.878 72.581 |
| expression | 7.3784 | 6.223 | 1.186 | 0.250 | -5.602 20.359 |
| Omnibus: | 0.529 | Durbin-Watson: | 1.424 |
| Prob(Omnibus): | 0.768 | Jarque-Bera (JB): | 0.601 |
| Skew: | -0.100 | Prob(JB): | 0.740 |
| Kurtosis: | 2.234 | Cond. No. | 37.8 |
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: | 04:47:17 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.03334 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.857 |
| Time: | 04:47:17 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.4349 | 51.348 | 1.372 | 0.185 | -36.349 177.219 |
| expression | 1.9165 | 10.497 | 0.183 | 0.857 | -19.912 23.745 |
| Omnibus: | 3.143 | Durbin-Watson: | 2.444 |
| Prob(Omnibus): | 0.208 | Jarque-Bera (JB): | 1.584 |
| Skew: | 0.318 | Prob(JB): | 0.453 |
| Kurtosis: | 1.882 | Cond. No. | 36.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.704 | 0.418 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.546 |
| Model: | OLS | Adj. R-squared: | 0.423 |
| Method: | Least Squares | F-statistic: | 4.418 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0286 |
| Time: | 04:47:17 | Log-Likelihood: | -69.370 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -51.1910 | 78.625 | -0.651 | 0.528 | -224.242 121.860 |
| C(dose)[T.1] | 184.0908 | 108.468 | 1.697 | 0.118 | -54.645 422.827 |
| expression | 22.8571 | 15.004 | 1.523 | 0.156 | -10.167 55.881 |
| expression:C(dose)[T.1] | -25.8491 | 20.257 | -1.276 | 0.228 | -70.434 18.736 |
| Omnibus: | 3.639 | Durbin-Watson: | 1.164 |
| Prob(Omnibus): | 0.162 | Jarque-Bera (JB): | 1.835 |
| Skew: | -0.846 | Prob(JB): | 0.399 |
| Kurtosis: | 3.273 | Cond. No. | 110. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.479 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 5.523 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0199 |
| Time: | 04:47:17 | Log-Likelihood: | -70.406 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 12 | BIC: | 148.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.4076 | 54.816 | 0.409 | 0.690 | -97.026 141.842 |
| C(dose)[T.1] | 47.0304 | 15.514 | 3.032 | 0.010 | 13.229 80.832 |
| expression | 8.6752 | 10.341 | 0.839 | 0.418 | -13.856 31.206 |
| Omnibus: | 1.633 | Durbin-Watson: | 0.888 |
| Prob(Omnibus): | 0.442 | Jarque-Bera (JB): | 1.283 |
| Skew: | -0.642 | Prob(JB): | 0.527 |
| Kurtosis: | 2.363 | Cond. No. | 40.1 |
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: | 04:47:17 | 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: | 0.081 |
| Model: | OLS | Adj. R-squared: | 0.010 |
| Method: | Least Squares | F-statistic: | 1.139 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.305 |
| Time: | 04:47:17 | Log-Likelihood: | -74.670 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 19.7196 | 69.975 | 0.282 | 0.783 | -131.453 170.892 |
| expression | 13.8925 | 13.018 | 1.067 | 0.305 | -14.232 42.017 |
| Omnibus: | 2.368 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.453 |
| Skew: | 0.515 | Prob(JB): | 0.483 |
| Kurtosis: | 1.875 | Cond. No. | 39.9 |