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 |
| 0.255 | 0.619 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.46e-05 |
| Time: | 03:58:52 | Log-Likelihood: | -100.41 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -195.7206 | 238.404 | -0.821 | 0.422 | -694.706 303.265 |
| C(dose)[T.1] | 298.7188 | 265.481 | 1.125 | 0.275 | -256.939 854.377 |
| expression | 35.8483 | 34.184 | 1.049 | 0.307 | -35.700 107.397 |
| expression:C(dose)[T.1] | -35.1832 | 38.204 | -0.921 | 0.369 | -115.146 44.780 |
| Omnibus: | 0.943 | Durbin-Watson: | 1.720 |
| Prob(Omnibus): | 0.624 | Jarque-Bera (JB): | 0.859 |
| Skew: | -0.249 | Prob(JB): | 0.651 |
| Kurtosis: | 2.194 | Cond. No. | 634. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.86 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.50e-05 |
| Time: | 03:58:52 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.6634 | 106.186 | 0.006 | 0.995 | -220.837 222.163 |
| C(dose)[T.1] | 54.3727 | 8.952 | 6.074 | 0.000 | 35.698 73.047 |
| expression | 7.6802 | 15.206 | 0.505 | 0.619 | -24.039 39.400 |
| Omnibus: | 0.155 | Durbin-Watson: | 1.849 |
| Prob(Omnibus): | 0.925 | Jarque-Bera (JB): | 0.370 |
| Skew: | 0.066 | Prob(JB): | 0.831 |
| Kurtosis: | 2.393 | Cond. No. | 173. |
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: | 03:58:52 | 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: | 0.014 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.3058 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.586 |
| Time: | 03:58:52 | Log-Likelihood: | -112.94 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 172.7755 | 168.431 | 1.026 | 0.317 | -177.497 523.048 |
| expression | -13.4723 | 24.362 | -0.553 | 0.586 | -64.136 37.192 |
| Omnibus: | 3.968 | Durbin-Watson: | 2.435 |
| Prob(Omnibus): | 0.138 | Jarque-Bera (JB): | 1.697 |
| Skew: | 0.291 | Prob(JB): | 0.428 |
| Kurtosis: | 1.803 | Cond. No. | 166. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.451 | 0.252 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.521 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 3.985 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0380 |
| Time: | 03:58:52 | Log-Likelihood: | -69.783 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -365.6030 | 459.005 | -0.797 | 0.443 | -1375.867 644.661 |
| C(dose)[T.1] | 310.9195 | 499.251 | 0.623 | 0.546 | -787.924 1409.763 |
| expression | 55.4523 | 58.761 | 0.944 | 0.366 | -73.880 184.784 |
| expression:C(dose)[T.1] | -34.1762 | 63.608 | -0.537 | 0.602 | -174.177 105.825 |
| Omnibus: | 2.421 | Durbin-Watson: | 0.857 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.394 |
| Skew: | -0.468 | Prob(JB): | 0.498 |
| Kurtosis: | 1.836 | Cond. No. | 821. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.426 |
| Method: | Least Squares | F-statistic: | 6.201 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0141 |
| Time: | 03:58:52 | Log-Likelihood: | -69.977 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -137.8459 | 170.757 | -0.807 | 0.435 | -509.894 234.202 |
| C(dose)[T.1] | 42.8192 | 15.781 | 2.713 | 0.019 | 8.435 77.203 |
| expression | 26.2866 | 21.822 | 1.205 | 0.252 | -21.260 73.833 |
| Omnibus: | 1.988 | Durbin-Watson: | 0.834 |
| Prob(Omnibus): | 0.370 | Jarque-Bera (JB): | 1.375 |
| Skew: | -0.533 | Prob(JB): | 0.503 |
| Kurtosis: | 1.968 | Cond. No. | 186. |
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: | 03:58:52 | 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: | 0.207 |
| Model: | OLS | Adj. R-squared: | 0.145 |
| Method: | Least Squares | F-statistic: | 3.384 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0888 |
| Time: | 03:58:52 | Log-Likelihood: | -73.565 |
| No. Observations: | 15 | AIC: | 151.1 |
| Df Residuals: | 13 | BIC: | 152.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -272.6979 | 199.372 | -1.368 | 0.195 | -703.415 158.019 |
| expression | 46.1506 | 25.089 | 1.839 | 0.089 | -8.050 100.352 |
| Omnibus: | 0.641 | Durbin-Watson: | 1.398 |
| Prob(Omnibus): | 0.726 | Jarque-Bera (JB): | 0.627 |
| Skew: | 0.190 | Prob(JB): | 0.731 |
| Kurtosis: | 2.074 | Cond. No. | 178. |