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 |
| 0.132 | 0.720 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.807 |
| Model: | OLS | Adj. R-squared: | 0.776 |
| Method: | Least Squares | F-statistic: | 26.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.39e-07 |
| Time: | 04:55:51 | Log-Likelihood: | -94.194 |
| No. Observations: | 23 | AIC: | 196.4 |
| Df Residuals: | 19 | BIC: | 200.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -86.8331 | 66.539 | -1.305 | 0.207 | -226.101 52.435 |
| C(dose)[T.1] | 459.7303 | 104.138 | 4.415 | 0.000 | 241.767 677.694 |
| expression | 25.5081 | 12.005 | 2.125 | 0.047 | 0.381 50.635 |
| expression:C(dose)[T.1] | -73.7191 | 18.846 | -3.912 | 0.001 | -113.165 -34.273 |
| Omnibus: | 0.968 | Durbin-Watson: | 2.117 |
| Prob(Omnibus): | 0.616 | Jarque-Bera (JB): | 0.783 |
| Skew: | -0.099 | Prob(JB): | 0.676 |
| Kurtosis: | 2.118 | Cond. No. | 222. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.65e-05 |
| Time: | 04:55:51 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.5581 | 67.283 | 1.168 | 0.257 | -61.792 218.908 |
| C(dose)[T.1] | 53.2256 | 8.746 | 6.085 | 0.000 | 34.981 71.470 |
| expression | -4.4038 | 12.119 | -0.363 | 0.720 | -29.684 20.877 |
| Omnibus: | 0.320 | Durbin-Watson: | 1.880 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.487 |
| Skew: | 0.111 | Prob(JB): | 0.784 |
| Kurtosis: | 2.322 | Cond. No. | 88.3 |
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: | 04:55:51 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1227 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.730 |
| Time: | 04:55:51 | Log-Likelihood: | -113.04 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 118.2899 | 110.358 | 1.072 | 0.296 | -111.212 347.791 |
| expression | -6.9913 | 19.960 | -0.350 | 0.730 | -48.500 34.518 |
| Omnibus: | 3.466 | Durbin-Watson: | 2.462 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.442 |
| Skew: | 0.164 | Prob(JB): | 0.486 |
| Kurtosis: | 1.818 | Cond. No. | 87.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.050 | 0.826 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.334 |
| Method: | Least Squares | F-statistic: | 3.344 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0595 |
| Time: | 04:55:51 | Log-Likelihood: | -70.439 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.4164 | 170.711 | -0.084 | 0.934 | -390.150 361.317 |
| C(dose)[T.1] | 199.7225 | 205.595 | 0.971 | 0.352 | -252.788 652.233 |
| expression | 16.7540 | 34.863 | 0.481 | 0.640 | -59.980 93.488 |
| expression:C(dose)[T.1] | -31.3573 | 42.468 | -0.738 | 0.476 | -124.828 62.113 |
| Omnibus: | 2.761 | Durbin-Watson: | 0.932 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.789 |
| Skew: | -0.835 | Prob(JB): | 0.409 |
| Kurtosis: | 2.735 | Cond. No. | 185. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.930 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0274 |
| Time: | 04:55:51 | Log-Likelihood: | -70.802 |
| 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 | 88.8203 | 96.076 | 0.924 | 0.373 | -120.512 298.153 |
| C(dose)[T.1] | 48.3995 | 16.104 | 3.005 | 0.011 | 13.312 83.487 |
| expression | -4.3790 | 19.527 | -0.224 | 0.826 | -46.924 38.166 |
| Omnibus: | 2.866 | Durbin-Watson: | 0.798 |
| Prob(Omnibus): | 0.239 | Jarque-Bera (JB): | 1.973 |
| Skew: | -0.869 | Prob(JB): | 0.373 |
| Kurtosis: | 2.627 | Cond. No. | 61.8 |
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: | 04:55:51 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.5117 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.487 |
| Time: | 04:55:51 | Log-Likelihood: | -75.011 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.6399 | 116.418 | 1.517 | 0.153 | -74.866 428.145 |
| expression | -17.3293 | 24.225 | -0.715 | 0.487 | -69.664 35.006 |
| Omnibus: | 0.289 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.865 | Jarque-Bera (JB): | 0.448 |
| Skew: | -0.084 | Prob(JB): | 0.799 |
| Kurtosis: | 2.170 | Cond. No. | 58.5 |