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 |
| 0.870 | 0.362 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 13.05 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 7.36e-05 |
| Time: | 11:47:08 | Log-Likelihood: | -100.24 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -63.2042 | 99.207 | -0.637 | 0.532 | -270.847 144.439 |
| C(dose)[T.1] | 170.9207 | 161.223 | 1.060 | 0.302 | -166.523 508.365 |
| expression | 17.3720 | 14.651 | 1.186 | 0.250 | -13.294 48.038 |
| expression:C(dose)[T.1] | -17.3965 | 23.338 | -0.745 | 0.465 | -66.243 31.450 |
| Omnibus: | 0.163 | Durbin-Watson: | 2.020 |
| Prob(Omnibus): | 0.922 | Jarque-Bera (JB): | 0.207 |
| Skew: | 0.164 | Prob(JB): | 0.902 |
| Kurtosis: | 2.672 | Cond. No. | 324. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.73 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 1.85e-05 |
| Time: | 11:47:08 | Log-Likelihood: | -100.57 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.8630 | 76.449 | -0.221 | 0.828 | -176.332 142.606 |
| C(dose)[T.1] | 50.9319 | 8.964 | 5.682 | 0.000 | 32.233 69.631 |
| expression | 10.5155 | 11.277 | 0.932 | 0.362 | -13.008 34.039 |
| Omnibus: | 0.273 | Durbin-Watson: | 1.961 |
| Prob(Omnibus): | 0.872 | Jarque-Bera (JB): | 0.153 |
| Skew: | 0.176 | Prob(JB): | 0.926 |
| Kurtosis: | 2.809 | Cond. No. | 126. |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 11:47:08 | 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: | 0.121 |
| Model: | OLS | Adj. R-squared: | 0.079 |
| Method: | Least Squares | F-statistic: | 2.886 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.104 |
| Time: | 11:47:08 | Log-Likelihood: | -111.62 |
| No. Observations: | 23 | AIC: | 227.2 |
| Df Residuals: | 21 | BIC: | 229.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -119.1225 | 117.233 | -1.016 | 0.321 | -362.922 124.677 |
| expression | 28.9512 | 17.041 | 1.699 | 0.104 | -6.487 64.389 |
| Omnibus: | 1.909 | Durbin-Watson: | 2.387 |
| Prob(Omnibus): | 0.385 | Jarque-Bera (JB): | 1.612 |
| Skew: | 0.527 | Prob(JB): | 0.447 |
| Kurtosis: | 2.244 | Cond. No. | 122. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.170 | 0.687 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.326 |
| Method: | Least Squares | F-statistic: | 3.252 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0636 |
| Time: | 11:47:08 | Log-Likelihood: | -70.538 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 242.1547 | 266.163 | 0.910 | 0.382 | -343.666 827.976 |
| C(dose)[T.1] | -111.2702 | 301.296 | -0.369 | 0.719 | -774.418 551.877 |
| expression | -20.3097 | 30.908 | -0.657 | 0.525 | -88.337 47.718 |
| expression:C(dose)[T.1] | 18.6283 | 35.083 | 0.531 | 0.606 | -58.589 95.846 |
| Omnibus: | 3.355 | Durbin-Watson: | 0.901 |
| Prob(Omnibus): | 0.187 | Jarque-Bera (JB): | 2.264 |
| Skew: | -0.940 | Prob(JB): | 0.322 |
| Kurtosis: | 2.703 | Cond. No. | 487. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.039 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.0258 |
| Time: | 11:47:08 | Log-Likelihood: | -70.727 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.7693 | 122.518 | 0.961 | 0.355 | -149.174 384.713 |
| C(dose)[T.1] | 48.4787 | 15.726 | 3.083 | 0.009 | 14.216 82.742 |
| expression | -5.8515 | 14.179 | -0.413 | 0.687 | -36.745 25.042 |
| Omnibus: | 2.974 | Durbin-Watson: | 0.772 |
| Prob(Omnibus): | 0.226 | Jarque-Bera (JB): | 2.167 |
| Skew: | -0.895 | Prob(JB): | 0.338 |
| Kurtosis: | 2.491 | Cond. No. | 137. |
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: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 11:47:08 | 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: | 0.026 |
| Model: | OLS | Adj. R-squared: | -0.049 |
| Method: | Least Squares | F-statistic: | 0.3476 |
| Date: | Tue, 03 Dec 2024 | Prob (F-statistic): | 0.566 |
| Time: | 11:47:08 | Log-Likelihood: | -75.102 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 184.8977 | 155.065 | 1.192 | 0.254 | -150.100 519.895 |
| expression | -10.6857 | 18.124 | -0.590 | 0.566 | -49.841 28.470 |
| Omnibus: | 0.796 | Durbin-Watson: | 1.662 |
| Prob(Omnibus): | 0.672 | Jarque-Bera (JB): | 0.650 |
| Skew: | 0.068 | Prob(JB): | 0.722 |
| Kurtosis: | 1.989 | Cond. No. | 134. |