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 |
| 5.221 | 0.033 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.726 |
| Model: | OLS | Adj. R-squared: | 0.683 |
| Method: | Least Squares | F-statistic: | 16.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.41e-05 |
| Time: | 04:34:47 | Log-Likelihood: | -98.199 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 19 | BIC: | 208.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.1058 | 48.406 | 2.481 | 0.023 | 18.791 221.421 |
| C(dose)[T.1] | 87.8302 | 72.523 | 1.211 | 0.241 | -63.963 239.624 |
| expression | -11.0505 | 8.065 | -1.370 | 0.187 | -27.930 5.829 |
| expression:C(dose)[T.1] | -7.2577 | 12.686 | -0.572 | 0.574 | -33.811 19.295 |
| Omnibus: | 1.947 | Durbin-Watson: | 2.046 |
| Prob(Omnibus): | 0.378 | Jarque-Bera (JB): | 1.102 |
| Skew: | 0.146 | Prob(JB): | 0.576 |
| Kurtosis: | 1.968 | Cond. No. | 135. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.722 |
| Model: | OLS | Adj. R-squared: | 0.694 |
| Method: | Least Squares | F-statistic: | 25.93 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.79e-06 |
| Time: | 04:34:47 | Log-Likelihood: | -98.395 |
| No. Observations: | 23 | AIC: | 202.8 |
| Df Residuals: | 20 | BIC: | 206.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.5962 | 36.892 | 3.730 | 0.001 | 60.640 214.552 |
| C(dose)[T.1] | 46.6258 | 8.344 | 5.588 | 0.000 | 29.221 64.030 |
| expression | -13.9835 | 6.120 | -2.285 | 0.033 | -26.749 -1.218 |
| Omnibus: | 3.702 | Durbin-Watson: | 2.145 |
| Prob(Omnibus): | 0.157 | Jarque-Bera (JB): | 1.407 |
| Skew: | -0.006 | Prob(JB): | 0.495 |
| Kurtosis: | 1.788 | Cond. No. | 56.6 |
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: | 04:34:47 | 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: | 0.287 |
| Model: | OLS | Adj. R-squared: | 0.253 |
| Method: | Least Squares | F-statistic: | 8.460 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00840 |
| Time: | 04:34:47 | Log-Likelihood: | -109.21 |
| No. Observations: | 23 | AIC: | 222.4 |
| Df Residuals: | 21 | BIC: | 224.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 228.9251 | 51.658 | 4.432 | 0.000 | 121.496 336.355 |
| expression | -26.0226 | 8.947 | -2.909 | 0.008 | -44.628 -7.417 |
| Omnibus: | 0.147 | Durbin-Watson: | 2.637 |
| Prob(Omnibus): | 0.929 | Jarque-Bera (JB): | 0.181 |
| Skew: | 0.151 | Prob(JB): | 0.913 |
| Kurtosis: | 2.687 | Cond. No. | 50.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.262 | 0.158 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.575 |
| Model: | OLS | Adj. R-squared: | 0.458 |
| Method: | Least Squares | F-statistic: | 4.951 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0205 |
| Time: | 04:34:47 | Log-Likelihood: | -68.891 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 11 | BIC: | 148.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.2576 | 68.711 | 1.707 | 0.116 | -33.975 268.490 |
| C(dose)[T.1] | 176.2777 | 127.900 | 1.378 | 0.196 | -105.228 457.783 |
| expression | -9.8181 | 13.378 | -0.734 | 0.478 | -39.263 19.627 |
| expression:C(dose)[T.1] | -24.7870 | 24.910 | -0.995 | 0.341 | -79.614 30.040 |
| Omnibus: | 2.002 | Durbin-Watson: | 1.073 |
| Prob(Omnibus): | 0.368 | Jarque-Bera (JB): | 1.370 |
| Skew: | -0.526 | Prob(JB): | 0.504 |
| Kurtosis: | 1.958 | Cond. No. | 115. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.536 |
| Model: | OLS | Adj. R-squared: | 0.459 |
| Method: | Least Squares | F-statistic: | 6.937 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00995 |
| Time: | 04:34:47 | Log-Likelihood: | -69.538 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 12 | BIC: | 147.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 153.5416 | 58.214 | 2.638 | 0.022 | 26.705 280.378 |
| C(dose)[T.1] | 49.8248 | 14.443 | 3.450 | 0.005 | 18.355 81.294 |
| expression | -16.9673 | 11.280 | -1.504 | 0.158 | -41.545 7.611 |
| Omnibus: | 3.213 | Durbin-Watson: | 1.408 |
| Prob(Omnibus): | 0.201 | Jarque-Bera (JB): | 1.556 |
| Skew: | -0.468 | Prob(JB): | 0.459 |
| Kurtosis: | 1.730 | Cond. No. | 43.3 |
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: | 04:34:47 | 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: | 0.076 |
| Model: | OLS | Adj. R-squared: | 0.005 |
| Method: | Least Squares | F-statistic: | 1.074 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.319 |
| Time: | 04:34:47 | Log-Likelihood: | -74.705 |
| No. Observations: | 15 | AIC: | 153.4 |
| Df Residuals: | 13 | BIC: | 154.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 174.3806 | 78.506 | 2.221 | 0.045 | 4.780 343.982 |
| expression | -15.8419 | 15.289 | -1.036 | 0.319 | -48.871 17.187 |
| Omnibus: | 0.738 | Durbin-Watson: | 1.710 |
| Prob(Omnibus): | 0.691 | Jarque-Bera (JB): | 0.647 |
| Skew: | 0.138 | Prob(JB): | 0.724 |
| Kurtosis: | 2.021 | Cond. No. | 42.8 |