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 |
| 0.433 | 0.518 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.57e-05 |
| Time: | 04:51:59 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.2774 | 82.930 | 0.787 | 0.441 | -108.298 238.853 |
| C(dose)[T.1] | 183.2796 | 160.462 | 1.142 | 0.268 | -152.572 519.131 |
| expression | -1.3998 | 10.459 | -0.134 | 0.895 | -23.291 20.492 |
| expression:C(dose)[T.1] | -16.0778 | 19.967 | -0.805 | 0.431 | -57.869 25.713 |
| Omnibus: | 0.089 | Durbin-Watson: | 2.091 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.315 |
| Skew: | 0.001 | Prob(JB): | 0.854 |
| Kurtosis: | 2.427 | Cond. No. | 354. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.29e-05 |
| Time: | 04:51:59 | Log-Likelihood: | -100.82 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.1642 | 70.089 | 1.429 | 0.168 | -46.038 246.367 |
| C(dose)[T.1] | 54.2696 | 8.791 | 6.173 | 0.000 | 35.931 72.608 |
| expression | -5.8115 | 8.831 | -0.658 | 0.518 | -24.232 12.609 |
| Omnibus: | 0.349 | Durbin-Watson: | 2.030 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.506 |
| Skew: | 0.111 | Prob(JB): | 0.777 |
| Kurtosis: | 2.308 | Cond. No. | 132. |
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: | 04:51:59 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.04209 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.839 |
| Time: | 04:52:00 | Log-Likelihood: | -113.08 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.9693 | 115.978 | 0.483 | 0.634 | -185.219 297.158 |
| expression | 2.9743 | 14.497 | 0.205 | 0.839 | -27.175 33.123 |
| Omnibus: | 2.879 | Durbin-Watson: | 2.478 |
| Prob(Omnibus): | 0.237 | Jarque-Bera (JB): | 1.483 |
| Skew: | 0.291 | Prob(JB): | 0.476 |
| Kurtosis: | 1.900 | Cond. No. | 131. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.728 | 0.213 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.528 |
| Model: | OLS | Adj. R-squared: | 0.400 |
| Method: | Least Squares | F-statistic: | 4.105 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0351 |
| Time: | 04:52:00 | Log-Likelihood: | -69.666 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 257.9002 | 170.330 | 1.514 | 0.158 | -116.993 632.794 |
| C(dose)[T.1] | -55.5229 | 203.639 | -0.273 | 0.790 | -503.728 392.683 |
| expression | -20.9947 | 18.735 | -1.121 | 0.286 | -62.229 20.240 |
| expression:C(dose)[T.1] | 11.0019 | 22.774 | 0.483 | 0.639 | -39.124 61.128 |
| Omnibus: | 2.317 | Durbin-Watson: | 0.717 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.550 |
| Skew: | -0.582 | Prob(JB): | 0.461 |
| Kurtosis: | 1.940 | Cond. No. | 345. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 6.453 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0125 |
| Time: | 04:52:00 | Log-Likelihood: | -69.824 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.3562 | 94.120 | 2.022 | 0.066 | -14.713 395.425 |
| C(dose)[T.1] | 42.5441 | 15.561 | 2.734 | 0.018 | 8.639 76.449 |
| expression | -13.5497 | 10.306 | -1.315 | 0.213 | -36.006 8.906 |
| Omnibus: | 2.319 | Durbin-Watson: | 0.680 |
| Prob(Omnibus): | 0.314 | Jarque-Bera (JB): | 1.546 |
| Skew: | -0.580 | Prob(JB): | 0.462 |
| Kurtosis: | 1.937 | Cond. No. | 115. |
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: | 04:52:00 | 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: | 0.218 |
| Model: | OLS | Adj. R-squared: | 0.158 |
| Method: | Least Squares | F-statistic: | 3.625 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0793 |
| Time: | 04:52:00 | Log-Likelihood: | -73.455 |
| No. Observations: | 15 | AIC: | 150.9 |
| Df Residuals: | 13 | BIC: | 152.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 293.7733 | 105.485 | 2.785 | 0.015 | 65.887 521.660 |
| expression | -22.7123 | 11.929 | -1.904 | 0.079 | -48.484 3.059 |
| Omnibus: | 1.491 | Durbin-Watson: | 1.593 |
| Prob(Omnibus): | 0.475 | Jarque-Bera (JB): | 1.047 |
| Skew: | 0.387 | Prob(JB): | 0.592 |
| Kurtosis: | 1.962 | Cond. No. | 105. |