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 |
| 1.325 | 0.263 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.93e-05 |
| Time: | 04:05:54 | Log-Likelihood: | -99.974 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.3846 | 84.131 | 0.207 | 0.838 | -158.704 193.473 |
| C(dose)[T.1] | -51.9606 | 138.005 | -0.377 | 0.711 | -340.807 236.886 |
| expression | 5.9979 | 13.669 | 0.439 | 0.666 | -22.612 34.608 |
| expression:C(dose)[T.1] | 17.2486 | 22.494 | 0.767 | 0.453 | -29.833 64.330 |
| Omnibus: | 0.058 | Durbin-Watson: | 1.927 |
| Prob(Omnibus): | 0.972 | Jarque-Bera (JB): | 0.173 |
| Skew: | -0.100 | Prob(JB): | 0.917 |
| Kurtosis: | 2.625 | Cond. No. | 249. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 20.38 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.49e-05 |
| Time: | 04:05:54 | Log-Likelihood: | -100.32 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -21.7199 | 66.220 | -0.328 | 0.746 | -159.853 116.413 |
| C(dose)[T.1] | 53.6560 | 8.498 | 6.314 | 0.000 | 35.930 71.382 |
| expression | 12.3672 | 10.743 | 1.151 | 0.263 | -10.043 34.778 |
| Omnibus: | 0.087 | Durbin-Watson: | 1.971 |
| Prob(Omnibus): | 0.958 | Jarque-Bera (JB): | 0.300 |
| Skew: | -0.074 | Prob(JB): | 0.861 |
| Kurtosis: | 2.460 | Cond. No. | 98.7 |
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: | 04:05:54 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.032 |
| Method: | Least Squares | F-statistic: | 0.3138 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.581 |
| Time: | 04:05:54 | Log-Likelihood: | -112.93 |
| 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 | 17.4919 | 111.319 | 0.157 | 0.877 | -214.009 248.993 |
| expression | 10.1557 | 18.131 | 0.560 | 0.581 | -27.549 47.860 |
| Omnibus: | 3.965 | Durbin-Watson: | 2.507 |
| Prob(Omnibus): | 0.138 | Jarque-Bera (JB): | 1.602 |
| Skew: | 0.228 | Prob(JB): | 0.449 |
| Kurtosis: | 1.790 | Cond. No. | 97.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.479 | 0.247 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.534 |
| Model: | OLS | Adj. R-squared: | 0.407 |
| Method: | Least Squares | F-statistic: | 4.202 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0329 |
| Time: | 04:05:54 | Log-Likelihood: | -69.573 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 150.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -158.3709 | 163.096 | -0.971 | 0.352 | -517.343 200.602 |
| C(dose)[T.1] | 227.5078 | 230.085 | 0.989 | 0.344 | -278.906 733.922 |
| expression | 35.2997 | 25.439 | 1.388 | 0.193 | -20.690 91.290 |
| expression:C(dose)[T.1] | -27.7156 | 36.279 | -0.764 | 0.461 | -107.566 52.134 |
| Omnibus: | 2.319 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.512 |
| Skew: | -0.763 | Prob(JB): | 0.470 |
| Kurtosis: | 2.695 | Cond. No. | 262. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 6.227 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0140 |
| Time: | 04:05:54 | Log-Likelihood: | -69.961 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -71.2044 | 114.501 | -0.622 | 0.546 | -320.681 178.272 |
| C(dose)[T.1] | 52.1233 | 15.045 | 3.465 | 0.005 | 19.344 84.903 |
| expression | 21.6728 | 17.820 | 1.216 | 0.247 | -17.153 60.498 |
| Omnibus: | 2.350 | Durbin-Watson: | 0.678 |
| Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.723 |
| Skew: | -0.787 | Prob(JB): | 0.422 |
| Kurtosis: | 2.472 | Cond. No. | 101. |
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: | 04:05:54 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.057 |
| Method: | Least Squares | F-statistic: | 0.2436 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.630 |
| Time: | 04:05:54 | Log-Likelihood: | -75.161 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 19.0522 | 151.506 | 0.126 | 0.902 | -308.257 346.362 |
| expression | 11.7975 | 23.902 | 0.494 | 0.630 | -39.840 63.435 |
| Omnibus: | 2.034 | Durbin-Watson: | 1.640 |
| Prob(Omnibus): | 0.362 | Jarque-Bera (JB): | 1.030 |
| Skew: | 0.218 | Prob(JB): | 0.597 |
| Kurtosis: | 1.793 | Cond. No. | 97.8 |