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 |
| 0.524 | 0.478 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 16.24 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 1.78e-05 |
| Time: | 17:38:00 | Log-Likelihood: | -98.490 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 19 | BIC: | 209.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.9392 | 26.489 | 2.829 | 0.011 | 19.497 130.381 |
| C(dose)[T.1] | -28.1746 | 40.391 | -0.698 | 0.494 | -112.714 56.365 |
| expression | -5.4956 | 6.865 | -0.800 | 0.433 | -19.865 8.874 |
| expression:C(dose)[T.1] | 20.7547 | 10.179 | 2.039 | 0.056 | -0.549 42.059 |
| Omnibus: | 0.121 | Durbin-Watson: | 1.363 |
| Prob(Omnibus): | 0.941 | Jarque-Bera (JB): | 0.100 |
| Skew: | 0.106 | Prob(JB): | 0.951 |
| Kurtosis: | 2.756 | Cond. No. | 53.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.24 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.19e-05 |
| Time: | 17:38:00 | Log-Likelihood: | -100.77 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.3210 | 21.428 | 1.835 | 0.081 | -5.376 84.018 |
| C(dose)[T.1] | 52.5043 | 8.733 | 6.012 | 0.000 | 34.287 70.722 |
| expression | 3.9466 | 5.454 | 0.724 | 0.478 | -7.431 15.324 |
| Omnibus: | 0.589 | Durbin-Watson: | 2.039 |
| Prob(Omnibus): | 0.745 | Jarque-Bera (JB): | 0.617 |
| Skew: | 0.012 | Prob(JB): | 0.735 |
| Kurtosis: | 2.198 | Cond. No. | 20.9 |
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, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 17:38:00 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.006 |
| Method: | Least Squares | F-statistic: | 0.8747 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.360 |
| Time: | 17:38:00 | Log-Likelihood: | -112.64 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.6945 | 34.961 | 1.364 | 0.187 | -25.012 120.401 |
| expression | 8.2679 | 8.840 | 0.935 | 0.360 | -10.116 26.652 |
| Omnibus: | 2.419 | Durbin-Watson: | 2.674 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.161 |
| Skew: | 0.021 | Prob(JB): | 0.560 |
| Kurtosis: | 1.900 | Cond. No. | 20.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.837 | 0.378 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.556 |
| Model: | OLS | Adj. R-squared: | 0.435 |
| Method: | Least Squares | F-statistic: | 4.586 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0257 |
| Time: | 17:38:00 | Log-Likelihood: | -69.215 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 11 | BIC: | 149.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.9807 | 42.544 | 1.598 | 0.138 | -25.658 161.620 |
| C(dose)[T.1] | -64.6052 | 82.192 | -0.786 | 0.448 | -245.508 116.298 |
| expression | -0.1240 | 9.241 | -0.013 | 0.990 | -20.462 20.215 |
| expression:C(dose)[T.1] | 20.8951 | 15.759 | 1.326 | 0.212 | -13.789 55.580 |
| Omnibus: | 1.126 | Durbin-Watson: | 1.060 |
| Prob(Omnibus): | 0.569 | Jarque-Bera (JB): | 0.950 |
| Skew: | -0.524 | Prob(JB): | 0.622 |
| Kurtosis: | 2.352 | Cond. No. | 74.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.399 |
| Method: | Least Squares | F-statistic: | 5.644 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0187 |
| Time: | 17:38:00 | Log-Likelihood: | -70.327 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.9809 | 36.127 | 0.996 | 0.339 | -42.733 114.694 |
| C(dose)[T.1] | 42.1469 | 17.058 | 2.471 | 0.029 | 4.981 79.312 |
| expression | 7.0608 | 7.718 | 0.915 | 0.378 | -9.755 23.877 |
| Omnibus: | 2.237 | Durbin-Watson: | 0.860 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.235 |
| Skew: | -0.382 | Prob(JB): | 0.539 |
| Kurtosis: | 1.821 | Cond. No. | 25.6 |
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, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 17:38:01 | 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: | 0.223 |
| Model: | OLS | Adj. R-squared: | 0.163 |
| Method: | Least Squares | F-statistic: | 3.721 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0758 |
| Time: | 17:38:01 | Log-Likelihood: | -73.412 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.5027 | 41.497 | 0.374 | 0.715 | -74.146 105.152 |
| expression | 15.6757 | 8.126 | 1.929 | 0.076 | -1.879 33.231 |
| Omnibus: | 1.900 | Durbin-Watson: | 1.214 |
| Prob(Omnibus): | 0.387 | Jarque-Bera (JB): | 0.928 |
| Skew: | -0.053 | Prob(JB): | 0.629 |
| Kurtosis: | 1.786 | Cond. No. | 24.5 |