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 |
| 14.033 | 0.001 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.794 |
| Model: | OLS | Adj. R-squared: | 0.762 |
| Method: | Least Squares | F-statistic: | 24.46 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.75e-07 |
| Time: | 06:24:17 | Log-Likelihood: | -94.919 |
| No. Observations: | 23 | AIC: | 197.8 |
| Df Residuals: | 19 | BIC: | 202.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -793.1265 | 374.866 | -2.116 | 0.048 | -1577.731 -8.522 |
| C(dose)[T.1] | 132.2914 | 460.036 | 0.288 | 0.777 | -830.575 1095.157 |
| expression | 96.9236 | 42.876 | 2.261 | 0.036 | 7.183 186.664 |
| expression:C(dose)[T.1] | -11.6207 | 52.100 | -0.223 | 0.826 | -120.668 97.427 |
| Omnibus: | 0.615 | Durbin-Watson: | 2.543 |
| Prob(Omnibus): | 0.735 | Jarque-Bera (JB): | 0.693 |
| Skew: | 0.290 | Prob(JB): | 0.707 |
| Kurtosis: | 2.378 | Cond. No. | 1.69e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.794 |
| Model: | OLS | Adj. R-squared: | 0.773 |
| Method: | Least Squares | F-statistic: | 38.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.39e-07 |
| Time: | 06:24:17 | Log-Likelihood: | -94.949 |
| No. Observations: | 23 | AIC: | 195.9 |
| Df Residuals: | 20 | BIC: | 199.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -724.3234 | 207.879 | -3.484 | 0.002 | -1157.952 -290.695 |
| C(dose)[T.1] | 29.7047 | 9.219 | 3.222 | 0.004 | 10.473 48.936 |
| expression | 89.0535 | 23.773 | 3.746 | 0.001 | 39.465 138.642 |
| Omnibus: | 0.695 | Durbin-Watson: | 2.484 |
| Prob(Omnibus): | 0.706 | Jarque-Bera (JB): | 0.748 |
| Skew: | 0.321 | Prob(JB): | 0.688 |
| Kurtosis: | 2.392 | Cond. No. | 557. |
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: | 06:24:17 | 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: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.672 |
| Method: | Least Squares | F-statistic: | 46.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.04e-06 |
| Time: | 06:24:17 | Log-Likelihood: | -99.757 |
| No. Observations: | 23 | AIC: | 203.5 |
| Df Residuals: | 21 | BIC: | 205.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1174.9763 | 184.976 | -6.352 | 0.000 | -1559.656 -790.297 |
| expression | 141.4662 | 20.851 | 6.785 | 0.000 | 98.104 184.828 |
| Omnibus: | 3.981 | Durbin-Watson: | 2.550 |
| Prob(Omnibus): | 0.137 | Jarque-Bera (JB): | 2.359 |
| Skew: | 0.750 | Prob(JB): | 0.307 |
| Kurtosis: | 3.460 | Cond. No. | 411. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.221 | 0.291 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.504 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 3.730 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0453 |
| Time: | 06:24:17 | Log-Likelihood: | -70.037 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -39.8791 | 764.521 | -0.052 | 0.959 | -1722.578 1642.820 |
| C(dose)[T.1] | -217.5264 | 836.681 | -0.260 | 0.800 | -2059.049 1623.996 |
| expression | 11.9099 | 84.843 | 0.140 | 0.891 | -174.829 198.649 |
| expression:C(dose)[T.1] | 29.6983 | 92.881 | 0.320 | 0.755 | -174.731 234.127 |
| Omnibus: | 0.453 | Durbin-Watson: | 0.871 |
| Prob(Omnibus): | 0.797 | Jarque-Bera (JB): | 0.472 |
| Skew: | -0.333 | Prob(JB): | 0.790 |
| Kurtosis: | 2.442 | Cond. No. | 1.51e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.416 |
| Method: | Least Squares | F-statistic: | 5.992 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0157 |
| Time: | 06:24:17 | Log-Likelihood: | -70.106 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -263.1534 | 299.400 | -0.879 | 0.397 | -915.490 389.183 |
| C(dose)[T.1] | 49.9529 | 15.011 | 3.328 | 0.006 | 17.247 82.659 |
| expression | 36.6907 | 33.208 | 1.105 | 0.291 | -35.662 109.044 |
| Omnibus: | 0.491 | Durbin-Watson: | 0.776 |
| Prob(Omnibus): | 0.782 | Jarque-Bera (JB): | 0.559 |
| Skew: | -0.322 | Prob(JB): | 0.756 |
| Kurtosis: | 2.308 | Cond. No. | 365. |
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: | 06:24:17 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.5129 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.487 |
| Time: | 06:24:17 | Log-Likelihood: | -75.010 |
| 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 | -191.1574 | 397.836 | -0.480 | 0.639 | -1050.630 668.315 |
| expression | 31.6507 | 44.195 | 0.716 | 0.487 | -63.827 127.129 |
| Omnibus: | 0.261 | Durbin-Watson: | 1.632 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.432 |
| Skew: | 0.155 | Prob(JB): | 0.806 |
| Kurtosis: | 2.228 | Cond. No. | 364. |